var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://api.zotero.org/groups/4799514/items?key=euYq5cdKpGK6xljQmpW8AeSb&format=bibtex&limit=100\",\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"How Does Knowledge Evolve in Open Knowledge Graphs?\",\"copyright\":\"https://creativecommons.org/licenses/by/4.0/legalcode\",\"url\":\"https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11\",\"doi\":\"10.4230/TGDK.1.1.11\",\"abstract\":\"Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.\",\"language\":\"en\",\"urldate\":\"2023-12-20\",\"journal\":\"DROPS-IDN/v2/document/10.4230/TGDK.1.1.11\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Polleres\"],\"firstnames\":[\"Axel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bonifati\"],\"firstnames\":[\"Angela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dell'Aglio\"],\"firstnames\":[\"Daniele\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dobriy\"],\"firstnames\":[\"Daniil\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dumbrava\"],\"firstnames\":[\"Stefania\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Etcheverry\"],\"firstnames\":[\"Lorena\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ferranti\"],\"firstnames\":[\"Nicolas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hose\"],\"firstnames\":[\"Katja\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jiménez-Ruiz\"],\"firstnames\":[\"Ernesto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lissandrini\"],\"firstnames\":[\"Matteo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Scherp\"],\"firstnames\":[\"Ansgar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tommasini\"],\"firstnames\":[\"Riccardo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wachs\"],\"firstnames\":[\"Johannes\"],\"suffixes\":[]}],\"year\":\"2023\",\"bibtex\":\"@article{polleres_how_2023,\\n\\ttitle = {How {Does} {Knowledge} {Evolve} in {Open} {Knowledge} {Graphs}?},\\n\\tcopyright = {https://creativecommons.org/licenses/by/4.0/legalcode},\\n\\turl = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},\\n\\tdoi = {10.4230/TGDK.1.1.11},\\n\\tabstract = {Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.},\\n\\tlanguage = {en},\\n\\turldate = {2023-12-20},\\n\\tjournal = {DROPS-IDN/v2/document/10.4230/TGDK.1.1.11},\\n\\tauthor = {Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jiménez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Polleres, A.\",\"Pernisch, R.\",\"Bonifati, A.\",\"Dell'Aglio, D.\",\"Dobriy, D.\",\"Dumbrava, S.\",\"Etcheverry, L.\",\"Ferranti, N.\",\"Hose, K.\",\"Jiménez-Ruiz, E.\",\"Lissandrini, M.\",\"Scherp, A.\",\"Tommasini, R.\",\"Wachs, J.\"],\"key\":\"polleres_how_2023\",\"id\":\"polleres_how_2023\",\"bibbaseid\":\"polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"series\":\"K-CAP '23\",\"title\":\"Do you catch my drift? On the usage of embedding methods to measure concept shift in knowledge graphs\",\"isbn\":\"9798400701412\",\"shorttitle\":\"Do you catch my drift?\",\"url\":\"https://dl.acm.org/doi/10.1145/3587259.3627555\",\"doi\":\"10.1145/3587259.3627555\",\"abstract\":\"Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.\",\"urldate\":\"2023-12-20\",\"booktitle\":\"Proceedings of the 12th Knowledge Capture Conference 2023\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Verkijk\"],\"firstnames\":[\"Stella\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Roothaert\"],\"firstnames\":[\"Ritten\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schlobach\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2023\",\"keywords\":\"Concept Shift, Knowledge Graph Embeddings, NLP, Semantics\",\"pages\":\"70–74\",\"bibtex\":\"@inproceedings{verkijk_you_2023,\\n\\taddress = {New York, NY, USA},\\n\\tseries = {K-{CAP} '23},\\n\\ttitle = {Do you catch my drift? {On} the usage of embedding methods to measure concept shift in knowledge graphs},\\n\\tisbn = {9798400701412},\\n\\tshorttitle = {Do you catch my drift?},\\n\\turl = {https://dl.acm.org/doi/10.1145/3587259.3627555},\\n\\tdoi = {10.1145/3587259.3627555},\\n\\tabstract = {Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.},\\n\\turldate = {2023-12-20},\\n\\tbooktitle = {Proceedings of the 12th {Knowledge} {Capture} {Conference} 2023},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Verkijk, Stella and Roothaert, Ritten and Pernisch, Romana and Schlobach, Stefan},\\n\\tmonth = dec,\\n\\tyear = {2023},\\n\\tkeywords = {Concept Shift, Knowledge Graph Embeddings, NLP, Semantics},\\n\\tpages = {70--74},\\n}\\n\\n\",\"author_short\":[\"Verkijk, S.\",\"Roothaert, R.\",\"Pernisch, R.\",\"Schlobach, S.\"],\"key\":\"verkijk_you_2023\",\"id\":\"verkijk_you_2023\",\"bibbaseid\":\"verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3587259.3627555\"},\"keyword\":[\"Concept Shift\",\"Knowledge Graph Embeddings\",\"NLP\",\"Semantics\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"series\":\"Frontiers in Artificial Intelligence and Applications\",\"title\":\"Approximate Answering of Graph Queries\",\"volume\":\"369\",\"isbn\":\"978-1-64368-407-9\",\"url\":\"https://doi.org/10.3233/FAIA230149\",\"booktitle\":\"Compendium of Neurosymbolic Artificial Intelligence\",\"publisher\":\"IOS Press\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Arakelyan\"],\"firstnames\":[\"Erik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Berrendorf\"],\"firstnames\":[\"Max\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Galkin\"],\"firstnames\":[\"Mikhail\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Minervini\"],\"firstnames\":[\"Pasquale\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Niepert\"],\"firstnames\":[\"Mathias\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ren\"],\"firstnames\":[\"Hongyu\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Hitzler\"],\"firstnames\":[\"Pascal\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sarker\"],\"firstnames\":[\"Md\",\"Kamruzzaman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Eberhart\"],\"firstnames\":[\"Aaron\"],\"suffixes\":[]}],\"year\":\"2023\",\"doi\":\"10.3233/FAIA230149\",\"pages\":\"373–386\",\"bibtex\":\"@incollection{cochez_approximate_2023,\\n\\tseries = {Frontiers in {Artificial} {Intelligence} and {Applications}},\\n\\ttitle = {Approximate {Answering} of {Graph} {Queries}},\\n\\tvolume = {369},\\n\\tisbn = {978-1-64368-407-9},\\n\\turl = {https://doi.org/10.3233/FAIA230149},\\n\\tbooktitle = {Compendium of {Neurosymbolic} {Artificial} {Intelligence}},\\n\\tpublisher = {IOS Press},\\n\\tauthor = {Cochez, Michael and Alivanistos, Dimitrios and Arakelyan, Erik and Berrendorf, Max and Daza, Daniel and Galkin, Mikhail and Minervini, Pasquale and Niepert, Mathias and Ren, Hongyu},\\n\\teditor = {Hitzler, Pascal and Sarker, Md Kamruzzaman and Eberhart, Aaron},\\n\\tyear = {2023},\\n\\tdoi = {10.3233/FAIA230149},\\n\\tpages = {373--386},\\n}\\n\\n\",\"author_short\":[\"Cochez, M.\",\"Alivanistos, D.\",\"Arakelyan, E.\",\"Berrendorf, M.\",\"Daza, D.\",\"Galkin, M.\",\"Minervini, P.\",\"Niepert, M.\",\"Ren, H.\"],\"editor_short\":[\"Hitzler, P.\",\"Sarker, M. K.\",\"Eberhart, A.\"],\"key\":\"cochez_approximate_2023\",\"id\":\"cochez_approximate_2023\",\"bibbaseid\":\"cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.3233/FAIA230149\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"A knowledge graph approach to predict and interpret disease-causing gene interactions\",\"volume\":\"24\",\"url\":\"https://doi.org/10.1186/s12859-023-05451-5\",\"doi\":\"10.1186/S12859-023-05451-5\",\"number\":\"1\",\"journal\":\"BMC Bioinformatics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Renaux\"],\"firstnames\":[\"Alexandre\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Terwagne\"],\"firstnames\":[\"Chloé\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tiddi\"],\"firstnames\":[\"Ilaria\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nowé\"],\"firstnames\":[\"Ann\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lenaerts\"],\"firstnames\":[\"Tom\"],\"suffixes\":[]}],\"year\":\"2023\",\"pages\":\"324\",\"bibtex\":\"@article{renaux_knowledge_2023,\\n\\ttitle = {A knowledge graph approach to predict and interpret disease-causing gene interactions},\\n\\tvolume = {24},\\n\\turl = {https://doi.org/10.1186/s12859-023-05451-5},\\n\\tdoi = {10.1186/S12859-023-05451-5},\\n\\tnumber = {1},\\n\\tjournal = {BMC Bioinformatics},\\n\\tauthor = {Renaux, Alexandre and Terwagne, Chloé and Cochez, Michael and Tiddi, Ilaria and Nowé, Ann and Lenaerts, Tom},\\n\\tyear = {2023},\\n\\tpages = {324},\\n}\\n\\n\",\"author_short\":[\"Renaux, A.\",\"Terwagne, C.\",\"Cochez, M.\",\"Tiddi, I.\",\"Nowé, A.\",\"Lenaerts, T.\"],\"key\":\"renaux_knowledge_2023\",\"id\":\"renaux_knowledge_2023\",\"bibbaseid\":\"renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1186/s12859-023-05451-5\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Explainable AI for Bioinformatics: Methods, Tools and Applications\",\"volume\":\"24\",\"url\":\"https://doi.org/10.1093/bib/bbad236\",\"doi\":\"10.1093/BIB/BBAD236\",\"number\":\"5\",\"journal\":\"Briefings Bioinformatics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Md\",\"Rezaul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Islam\"],\"firstnames\":[\"Tanhim\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shajalal\"],\"firstnames\":[\"Md\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beyan\"],\"firstnames\":[\"Oya\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lange\"],\"firstnames\":[\"Christoph\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rebholz-Schuhmann\"],\"firstnames\":[\"Dietrich\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2023\",\"bibtex\":\"@article{karim_explainable_2023,\\n\\ttitle = {Explainable {AI} for {Bioinformatics}: {Methods}, {Tools} and {Applications}},\\n\\tvolume = {24},\\n\\turl = {https://doi.org/10.1093/bib/bbad236},\\n\\tdoi = {10.1093/BIB/BBAD236},\\n\\tnumber = {5},\\n\\tjournal = {Briefings Bioinformatics},\\n\\tauthor = {Karim, Md Rezaul and Islam, Tanhim and Shajalal, Md and Beyan, Oya and Lange, Christoph and Cochez, Michael and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\\n\\tmonth = oct,\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Karim, M. R.\",\"Islam, T.\",\"Shajalal, M.\",\"Beyan, O.\",\"Lange, C.\",\"Cochez, M.\",\"Rebholz-Schuhmann, D.\",\"Decker, S.\"],\"key\":\"karim_explainable_2023\",\"id\":\"karim_explainable_2023\",\"bibbaseid\":\"karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1093/bib/bbad236\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Athens, Greece\",\"title\":\"Qualifier Recommendation for Wikidata\",\"url\":\"https://wikidataworkshop.github.io/2023/#mu-sessions\",\"abstract\":\"Wikidata, a collaborative knowledge base for structured data, empowers both human and machine users to contribute and access information. Its main role is in supporting Wikimedia projects by acting as the central storage database for the Wikimedia movement. To optimize the manual process of adding new facts, Wikidata utilizes the association rule-based PropertySuggester tool. However, a recent paper introduced the SchemaTree, a novel approach that surpasses the state-of-the-art PropertySuggester in all performance metrics. The new recommender employs a trie-based method and frequentist inference to efficiently learn and represent property set probabilities within RDF graphs. In this paper, we adapt that recommendation approach, to recommend qualifiers. Specifically, we want to find out whether the recommendation can be done using co-occurrence information of the qualifiers, or whether type information of the item and the value of statements improves performance. We found that the qualifier recommender that uses co-occurring qualifiers and type information leads to the best performance.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Ducu\"],\"firstnames\":[\"Andrei\",\"Mihai\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2023\",\"bibtex\":\"@inproceedings{ducu_qualifier_2023,\\n\\taddress = {Athens, Greece},\\n\\ttitle = {Qualifier {Recommendation} for {Wikidata}},\\n\\turl = {https://wikidataworkshop.github.io/2023/#mu-sessions},\\n\\tabstract = {Wikidata, a collaborative knowledge base for structured data, empowers both human and machine users to contribute and access information. Its main role is in supporting Wikimedia projects by acting as the central storage database for the Wikimedia movement. To optimize the manual process of adding new facts, Wikidata utilizes the association rule-based PropertySuggester tool. However, a recent paper introduced the SchemaTree, a novel approach that surpasses the state-of-the-art PropertySuggester in all performance metrics. The new recommender employs a trie-based method and frequentist inference to efficiently learn and represent property set probabilities within RDF graphs. In this paper, we adapt that recommendation approach, to recommend qualifiers. Specifically, we want to find out whether the recommendation can be done using co-occurrence information of the qualifiers, or whether type information of the item and the value of statements improves performance. We found that the qualifier recommender that uses co-occurring qualifiers and type information leads to the best performance.},\\n\\tauthor = {Ducu, Andrei Mihai and Cochez, Michael},\\n\\tmonth = nov,\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Ducu, A. M.\",\"Cochez, M.\"],\"key\":\"ducu_qualifier_2023\",\"id\":\"ducu_qualifier_2023\",\"bibbaseid\":\"ducu-cochez-qualifierrecommendationforwikidata-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://wikidataworkshop.github.io/2023/#mu-sessions\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Knowledge Graph Embeddings: Open Challenges and Opportunities\",\"volume\":\"1\",\"number\":\"1\",\"journal\":\"Transactions on Graph Data and Knowledge\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Biswas\"],\"firstnames\":[\"Russa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kaffee\"],\"firstnames\":[\"Lucie-Aimée\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dumbrava\"],\"firstnames\":[\"Stefania\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jendal\"],\"firstnames\":[\"Theis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lissandrini\"],\"firstnames\":[\"Matteo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lopez\"],\"firstnames\":[\"Vanessa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Menc\"],\"firstnames\":[\"Eneldo\",\"Loza\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Paulheim\"],\"firstnames\":[\"Heiko\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sack\"],\"firstnames\":[\"Harald\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vakaj\"],\"firstnames\":[\"Edlira\",\"Kalemi\"],\"suffixes\":[]},{\"propositions\":[\"de\"],\"lastnames\":[\"Melo\"],\"firstnames\":[\"Gerard\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2023\",\"bibtex\":\"@article{biswas_knowledge_2023,\\n\\ttitle = {Knowledge {Graph} {Embeddings}: {Open} {Challenges} and {Opportunities}},\\n\\tvolume = {1},\\n\\tnumber = {1},\\n\\tjournal = {Transactions on Graph Data and Knowledge},\\n\\tauthor = {Biswas, Russa and Kaffee, Lucie-Aimée and Cochez, Michael and Dumbrava, Stefania and Jendal, Theis and Lissandrini, Matteo and Lopez, Vanessa and Menc, Eneldo Loza and Paulheim, Heiko and Sack, Harald and Vakaj, Edlira Kalemi and de Melo, Gerard},\\n\\tmonth = dec,\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Biswas, R.\",\"Kaffee, L.\",\"Cochez, M.\",\"Dumbrava, S.\",\"Jendal, T.\",\"Lissandrini, M.\",\"Lopez, V.\",\"Menc, E. L.\",\"Paulheim, H.\",\"Sack, H.\",\"Vakaj, E. K.\",\"de Melo, G.\"],\"key\":\"biswas_knowledge_2023\",\"id\":\"biswas_knowledge_2023\",\"bibbaseid\":\"biswas-kaffee-cochez-dumbrava-jendal-lissandrini-lopez-menc-etal-knowledgegraphembeddingsopenchallengesandopportunities-2023\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New Orleans\",\"title\":\"Adapting Neural Link Predictors for Data-Efficient Complex Query Answering\",\"url\":\"https://openreview.net/forum?id=1G7CBp8o7L\",\"doi\":\"10.48550/arXiv.2301.12313\",\"abstract\":\"Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD , a parameter-efficient score \\\\emph\\\\adaptation\\\\ model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component – which only increases the number of model parameters by – is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD produces significantly more accurate results than current state-of-the-art methods, improving from to Mean Reciprocal Rank values averaged across all datasets and query types while using of the available training query types. We further show that CQD is data-efficient, achieving competitive results with only of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.\",\"publisher\":\"OpenReview.net\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Arakelyan\"],\"firstnames\":[\"Erik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Minervini\"],\"firstnames\":[\"Pasquale\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Augenstein\"],\"firstnames\":[\"Isabelle\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2023\",\"note\":\"arXiv:2301.12313 [cs]\",\"keywords\":\"Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing\",\"bibtex\":\"@inproceedings{arakelyan_adapting_2023,\\n\\taddress = {New Orleans},\\n\\ttitle = {Adapting {Neural} {Link} {Predictors} for {Data}-{Efficient} {Complex} {Query} {Answering}},\\n\\turl = {https://openreview.net/forum?id=1G7CBp8o7L},\\n\\tdoi = {10.48550/arXiv.2301.12313},\\n\\tabstract = {Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD\\n, a parameter-efficient score {\\\\textbackslash}emph\\\\{adaptation\\\\} model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component -- which only increases the number of model parameters by \\n -- is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD\\n produces significantly more accurate results than current state-of-the-art methods, improving from \\n to \\n Mean Reciprocal Rank values averaged across all datasets and query types while using \\n of the available training query types. We further show that CQD\\n is data-efficient, achieving competitive results with only \\n of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.},\\n\\tpublisher = {OpenReview.net},\\n\\tauthor = {Arakelyan, Erik and Minervini, Pasquale and Daza, Daniel and Cochez, Michael and Augenstein, Isabelle},\\n\\tmonth = jul,\\n\\tyear = {2023},\\n\\tnote = {arXiv:2301.12313 [cs]},\\n\\tkeywords = {Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},\\n}\\n\\n\",\"author_short\":[\"Arakelyan, E.\",\"Minervini, P.\",\"Daza, D.\",\"Cochez, M.\",\"Augenstein, I.\"],\"key\":\"arakelyan_adapting_2023\",\"id\":\"arakelyan_adapting_2023\",\"bibbaseid\":\"arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://openreview.net/forum?id=1G7CBp8o7L\"},\"keyword\":[\"Computer Science - Artificial Intelligence\",\"Computer Science - Logic in Computer Science\",\"Computer Science - Machine Learning\",\"Computer Science - Neural and Evolutionary Computing\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings\",\"url\":\"https://doi.org/10.1145/3583780.3615294\",\"doi\":\"10.1145/3583780.3615294\",\"booktitle\":\"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Xiong\"],\"firstnames\":[\"Bo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nayyeri\"],\"firstnames\":[\"Mojtaba\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Frommholz\"],\"firstnames\":[\"Ingo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hopfgartner\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lee\"],\"firstnames\":[\"Mark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Oakes\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lalmas\"],\"firstnames\":[\"Mounia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Min\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Santos\"],\"firstnames\":[\"Rodrygo\",\"L.\",\"T.\"],\"suffixes\":[]}],\"year\":\"2023\",\"pages\":\"5228–5231\",\"bibtex\":\"@inproceedings{xiong_reasoning_2023,\\n\\ttitle = {Reasoning beyond {Triples}: {Recent} {Advances} in {Knowledge} {Graph} {Embeddings}},\\n\\turl = {https://doi.org/10.1145/3583780.3615294},\\n\\tdoi = {10.1145/3583780.3615294},\\n\\tbooktitle = {Proceedings of the 32nd {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}, {CIKM} 2023, {Birmingham}, {United} {Kingdom}, {October} 21-25, 2023},\\n\\tpublisher = {ACM},\\n\\tauthor = {Xiong, Bo and Nayyeri, Mojtaba and Daza, Daniel and Cochez, Michael},\\n\\teditor = {Frommholz, Ingo and Hopfgartner, Frank and Lee, Mark and Oakes, Michael and Lalmas, Mounia and Zhang, Min and Santos, Rodrygo L. T.},\\n\\tyear = {2023},\\n\\tpages = {5228--5231},\\n}\\n\\n\",\"author_short\":[\"Xiong, B.\",\"Nayyeri, M.\",\"Daza, D.\",\"Cochez, M.\"],\"editor_short\":[\"Frommholz, I.\",\"Hopfgartner, F.\",\"Lee, M.\",\"Oakes, M.\",\"Lalmas, M.\",\"Zhang, M.\",\"Santos, R. L. T.\"],\"key\":\"xiong_reasoning_2023\",\"id\":\"xiong_reasoning_2023\",\"bibbaseid\":\"xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1145/3583780.3615294\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Structural Summarization of Semantic Graphs Using Quotients\",\"volume\":\"1\",\"abstract\":\"Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features. Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.\",\"number\":\"1\",\"journal\":\"Transactions on Graph Data and Knowledge\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Scherp\"],\"firstnames\":[\"Ansgar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Richerby\"],\"firstnames\":[\"David\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Blume\"],\"firstnames\":[\"Till\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rau\"],\"firstnames\":[\"Jannik\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2023\",\"bibtex\":\"@article{scherp_structural_2023,\\n\\ttitle = {Structural {Summarization} of {Semantic} {Graphs} {Using} {Quotients}},\\n\\tvolume = {1},\\n\\tabstract = {Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. \\nWe consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. \\n\\nIn this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features.\\nStructural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.},\\n\\tnumber = {1},\\n\\tjournal = {Transactions on Graph Data and Knowledge},\\n\\tauthor = {Scherp, Ansgar and Richerby, David and Blume, Till and Cochez, Michael and Rau, Jannik},\\n\\tmonth = dec,\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Scherp, A.\",\"Richerby, D.\",\"Blume, T.\",\"Cochez, M.\",\"Rau, J.\"],\"key\":\"scherp_structural_2023\",\"id\":\"scherp_structural_2023\",\"bibbaseid\":\"scherp-richerby-blume-cochez-rau-structuralsummarizationofsemanticgraphsusingquotients-2023\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs\",\"volume\":\"14\",\"issn\":\"2041-1480\",\"url\":\"https://doi.org/10.1186/s13326-023-00301-y\",\"doi\":\"10.1186/s13326-023-00301-y\",\"abstract\":\"Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain.\",\"number\":\"1\",\"journal\":\"Journal of Biomedical Semantics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mitra\"],\"firstnames\":[\"Payal\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pijnenburg\"],\"firstnames\":[\"Thom\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Groth\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2023\",\"pages\":\"20\",\"bibtex\":\"@article{daza_bioblp_2023,\\n\\ttitle = {{BioBLP}: a modular framework for learning on multimodal biomedical knowledge graphs},\\n\\tvolume = {14},\\n\\tissn = {2041-1480},\\n\\turl = {https://doi.org/10.1186/s13326-023-00301-y},\\n\\tdoi = {10.1186/s13326-023-00301-y},\\n\\tabstract = {Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain.},\\n\\tnumber = {1},\\n\\tjournal = {Journal of Biomedical Semantics},\\n\\tauthor = {Daza, Daniel and Alivanistos, Dimitrios and Mitra, Payal and Pijnenburg, Thom and Cochez, Michael and Groth, Paul},\\n\\tmonth = dec,\\n\\tyear = {2023},\\n\\tpages = {20},\\n}\\n\\n\",\"author_short\":[\"Daza, D.\",\"Alivanistos, D.\",\"Mitra, P.\",\"Pijnenburg, T.\",\"Cochez, M.\",\"Groth, P.\"],\"key\":\"daza_bioblp_2023\",\"id\":\"daza_bioblp_2023\",\"bibbaseid\":\"daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1186/s13326-023-00301-y\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"Descriptive Comparison of Visual Ontology Change Summarisation Methods\",\"volume\":\"13998\",\"isbn\":\"9783031434570 9783031434587\",\"url\":\"https://link.springer.com/10.1007/978-3-031-43458-7_10\",\"language\":\"en\",\"urldate\":\"2023-11-13\",\"booktitle\":\"The Semantic Web: ESWC 2023 Satellite Events\",\"publisher\":\"Springer Nature Switzerland\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chung\"],\"firstnames\":[\"Kornpol\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schlobach\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Pesquita\"],\"firstnames\":[\"Catia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Skaf-Molli\"],\"firstnames\":[\"Hala\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Efthymiou\"],\"firstnames\":[\"Vasilis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirrane\"],\"firstnames\":[\"Sabrina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ngonga\"],\"firstnames\":[\"Axel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Collarana\"],\"firstnames\":[\"Diego\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cerqueira\"],\"firstnames\":[\"Renato\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alam\"],\"firstnames\":[\"Mehwish\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Trojahn\"],\"firstnames\":[\"Cassia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hertling\"],\"firstnames\":[\"Sven\"],\"suffixes\":[]}],\"year\":\"2023\",\"doi\":\"10.1007/978-3-031-43458-7_10\",\"pages\":\"54–58\",\"bibtex\":\"@inproceedings{pesquita_descriptive_2023,\\n\\taddress = {Cham},\\n\\ttitle = {Descriptive {Comparison} of {Visual} {Ontology} {Change} {Summarisation} {Methods}},\\n\\tvolume = {13998},\\n\\tisbn = {9783031434570 9783031434587},\\n\\turl = {https://link.springer.com/10.1007/978-3-031-43458-7_10},\\n\\tlanguage = {en},\\n\\turldate = {2023-11-13},\\n\\tbooktitle = {The {Semantic} {Web}: {ESWC} 2023 {Satellite} {Events}},\\n\\tpublisher = {Springer Nature Switzerland},\\n\\tauthor = {Chung, Kornpol and Pernisch, Romana and Schlobach, Stefan},\\n\\teditor = {Pesquita, Catia and Skaf-Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, Cassia and Hertling, Sven},\\n\\tyear = {2023},\\n\\tdoi = {10.1007/978-3-031-43458-7_10},\\n\\tpages = {54--58},\\n}\\n\\n\",\"author_short\":[\"Chung, K.\",\"Pernisch, R.\",\"Schlobach, S.\"],\"editor_short\":[\"Pesquita, C.\",\"Skaf-Molli, H.\",\"Efthymiou, V.\",\"Kirrane, S.\",\"Ngonga, A.\",\"Collarana, D.\",\"Cerqueira, R.\",\"Alam, M.\",\"Trojahn, C.\",\"Hertling, S.\"],\"key\":\"pesquita_descriptive_2023\",\"id\":\"pesquita_descriptive_2023\",\"bibbaseid\":\"chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://link.springer.com/10.1007/978-3-031-43458-7_10\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe\",\"doi\":\"10.1109/CloudSummit54781.2022.00010\",\"abstract\":\"The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.\",\"booktitle\":\"2022 IEEE Cloud Summit\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Prodan\"],\"firstnames\":[\"Radu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kimovski\"],\"firstnames\":[\"Dragi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bartolini\"],\"firstnames\":[\"Andrea\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Iosup\"],\"firstnames\":[\"Alexandru\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kharlamov\"],\"firstnames\":[\"Evgeny\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rožanec\"],\"firstnames\":[\"Jože\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vasiliu\"],\"firstnames\":[\"Laurenţiu\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vărbănescu\"],\"firstnames\":[\"Ana\",\"Lucia\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2022\",\"keywords\":\"Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability\",\"pages\":\"23–30\",\"bibtex\":\"@inproceedings{prodan_towards_2022,\\n\\ttitle = {Towards {Extreme} and {Sustainable} {Graph} {Processing} for {Urgent} {Societal} {Challenges} in {Europe}},\\n\\tdoi = {10.1109/CloudSummit54781.2022.00010},\\n\\tabstract = {The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70\\\\% more efficient analytics than AliGraph, and 30 \\\\% improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 \\\\% lower greenhouse gas emissions for basic graph operations.},\\n\\tbooktitle = {2022 {IEEE} {Cloud} {Summit}},\\n\\tauthor = {Prodan, Radu and Kimovski, Dragi and Bartolini, Andrea and Cochez, Michael and Iosup, Alexandru and Kharlamov, Evgeny and Rožanec, Jože and Vasiliu, Laurenţiu and Vărbănescu, Ana Lucia},\\n\\tmonth = oct,\\n\\tyear = {2022},\\n\\tkeywords = {Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability},\\n\\tpages = {23--30},\\n}\\n\\n\",\"author_short\":[\"Prodan, R.\",\"Kimovski, D.\",\"Bartolini, A.\",\"Cochez, M.\",\"Iosup, A.\",\"Kharlamov, E.\",\"Rožanec, J.\",\"Vasiliu, L.\",\"Vărbănescu, A. L.\"],\"key\":\"prodan_towards_2022\",\"id\":\"prodan_towards_2022\",\"bibbaseid\":\"prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Cognition\",\"Data centers\",\"Europe\",\"Extreme data\",\"Green products\",\"Serverless computing\",\"Technological innovation\",\"Transforms\",\"graph processing\",\"serverless computing\",\"sustainability\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"series\":\"CIKM '22\",\"title\":\"Intersection of Parallels as an Early Stopping Criterion\",\"isbn\":\"978-1-4503-9236-5\",\"url\":\"https://dl.acm.org/doi/10.1145/3511808.3557366\",\"doi\":\"10.1145/3511808.3557366\",\"abstract\":\"A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in information retrieval. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.\",\"urldate\":\"2023-04-21\",\"booktitle\":\"Proceedings of the 31st ACM International Conference on Information & Knowledge Management\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Vardasbi\"],\"firstnames\":[\"Ali\"],\"suffixes\":[]},{\"propositions\":[\"de\"],\"lastnames\":[\"Rijke\"],\"firstnames\":[\"Maarten\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dehghani\"],\"firstnames\":[\"Mostafa\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2022\",\"keywords\":\"cosine distance, early stopping, generalization, overparameterization\",\"pages\":\"1965–1974\",\"bibtex\":\"@inproceedings{vardasbi_intersection_2022,\\n\\taddress = {New York, NY, USA},\\n\\tseries = {{CIKM} '22},\\n\\ttitle = {Intersection of {Parallels} as an {Early} {Stopping} {Criterion}},\\n\\tisbn = {978-1-4503-9236-5},\\n\\turl = {https://dl.acm.org/doi/10.1145/3511808.3557366},\\n\\tdoi = {10.1145/3511808.3557366},\\n\\tabstract = {A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in information retrieval. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.},\\n\\turldate = {2023-04-21},\\n\\tbooktitle = {Proceedings of the 31st {ACM} {International} {Conference} on {Information} \\\\& {Knowledge} {Management}},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Vardasbi, Ali and de Rijke, Maarten and Dehghani, Mostafa},\\n\\tmonth = oct,\\n\\tyear = {2022},\\n\\tkeywords = {cosine distance, early stopping, generalization, overparameterization},\\n\\tpages = {1965--1974},\\n}\\n\\n\",\"author_short\":[\"Vardasbi, A.\",\"de Rijke, M.\",\"Dehghani, M.\"],\"key\":\"vardasbi_intersection_2022\",\"id\":\"vardasbi_intersection_2022\",\"bibbaseid\":\"vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3511808.3557366\"},\"keyword\":[\"cosine distance\",\"early stopping\",\"generalization\",\"overparameterization\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"series\":\"SIGIR '22\",\"title\":\"Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking\",\"isbn\":\"978-1-4503-8732-3\",\"shorttitle\":\"Probabilistic Permutation Graph Search\",\"url\":\"https://doi.org/10.1145/3477495.3532045\",\"doi\":\"10.1145/3477495.3532045\",\"abstract\":\"There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \\\\acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \\\\acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to~\\\\acPL, our distribution representation, called~\\\\acPPG, can be used for black-box optimization of fairness. Different from~\\\\acPL, where pointwise logits are used as the distribution parameters, in~\\\\acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making~\\\\acPPG suitable for both deterministic and stochastic rankings. Our experiments show that~\\\\acPPG, while comparable to~\\\\acPL for larger session repetitions (i.e., stochastic ranking), improves over~\\\\acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of \\\\acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as \\\"item $d_1$ should always be ranked higher than item $d_2$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.\",\"urldate\":\"2023-04-21\",\"booktitle\":\"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Vardasbi\"],\"firstnames\":[\"Ali\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sarvi\"],\"firstnames\":[\"Fatemeh\"],\"suffixes\":[]},{\"propositions\":[\"de\"],\"lastnames\":[\"Rijke\"],\"firstnames\":[\"Maarten\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2022\",\"keywords\":\"fairness in ranking, permutation distribution, permutation graph, plackett-luce\",\"pages\":\"715–725\",\"bibtex\":\"@inproceedings{vardasbi_probabilistic_2022,\\n\\taddress = {New York, NY, USA},\\n\\tseries = {{SIGIR} '22},\\n\\ttitle = {Probabilistic {Permutation} {Graph} {Search}: {Black}-{Box} {Optimization} for {Fairness} in {Ranking}},\\n\\tisbn = {978-1-4503-8732-3},\\n\\tshorttitle = {Probabilistic {Permutation} {Graph} {Search}},\\n\\turl = {https://doi.org/10.1145/3477495.3532045},\\n\\tdoi = {10.1145/3477495.3532045},\\n\\tabstract = {There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. {\\\\textbackslash}acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, {\\\\textbackslash}acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to{\\\\textasciitilde}{\\\\textbackslash}acPL, our distribution representation, called{\\\\textasciitilde}{\\\\textbackslash}acPPG, can be used for black-box optimization of fairness. Different from{\\\\textasciitilde}{\\\\textbackslash}acPL, where pointwise logits are used as the distribution parameters, in{\\\\textasciitilde}{\\\\textbackslash}acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making{\\\\textasciitilde}{\\\\textbackslash}acPPG suitable for both deterministic and stochastic rankings. Our experiments show that{\\\\textasciitilde}{\\\\textbackslash}acPPG, while comparable to{\\\\textasciitilde}{\\\\textbackslash}acPL for larger session repetitions (i.e., stochastic ranking), improves over{\\\\textasciitilde}{\\\\textbackslash}acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of {\\\\textbackslash}acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as \\\"item \\\\$d\\\\_1\\\\$ should always be ranked higher than item \\\\$d\\\\_2\\\\$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.},\\n\\turldate = {2023-04-21},\\n\\tbooktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Vardasbi, Ali and Sarvi, Fatemeh and de Rijke, Maarten},\\n\\tmonth = jul,\\n\\tyear = {2022},\\n\\tkeywords = {fairness in ranking, permutation distribution, permutation graph, plackett-luce},\\n\\tpages = {715--725},\\n}\\n\\n\",\"author_short\":[\"Vardasbi, A.\",\"Sarvi, F.\",\"de Rijke, M.\"],\"key\":\"vardasbi_probabilistic_2022\",\"id\":\"vardasbi_probabilistic_2022\",\"bibbaseid\":\"vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1145/3477495.3532045\"},\"keyword\":[\"fairness in ranking\",\"permutation distribution\",\"permutation graph\",\"plackett-luce\"],\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Recommending scientific datasets using author networks in ensemble methods\",\"volume\":\"5\",\"issn\":\"2451-8484\",\"url\":\"https://content.iospress.com/articles/data-science/ds220056\",\"doi\":\"10.3233/DS-220056\",\"abstract\":\"Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation : the task of re\",\"language\":\"en\",\"number\":\"2\",\"urldate\":\"2023-04-22\",\"journal\":\"Data Science\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Xu\"],\"suffixes\":[]},{\"propositions\":[\"van\"],\"lastnames\":[\"Harmelen\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Huang\"],\"firstnames\":[\"Zhisheng\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2022\",\"note\":\"Publisher: IOS Press\",\"pages\":\"167–193\",\"bibtex\":\"@article{wang_recommending_2022,\\n\\ttitle = {Recommending scientific datasets using author networks in ensemble methods},\\n\\tvolume = {5},\\n\\tissn = {2451-8484},\\n\\turl = {https://content.iospress.com/articles/data-science/ds220056},\\n\\tdoi = {10.3233/DS-220056},\\n\\tabstract = {Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation : the task of re},\\n\\tlanguage = {en},\\n\\tnumber = {2},\\n\\turldate = {2023-04-22},\\n\\tjournal = {Data Science},\\n\\tauthor = {Wang, Xu and van Harmelen, Frank and Huang, Zhisheng},\\n\\tmonth = jul,\\n\\tyear = {2022},\\n\\tnote = {Publisher: IOS Press},\\n\\tpages = {167--193},\\n}\\n\\n\",\"author_short\":[\"Wang, X.\",\"van Harmelen, F.\",\"Huang, Z.\"],\"key\":\"wang_recommending_2022\",\"id\":\"wang_recommending_2022\",\"bibbaseid\":\"wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://content.iospress.com/articles/data-science/ds220056\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data\",\"isbn\":\"978-3-030-82472-3\",\"url\":\"https://www.cochez.nl/papers/feature_selection_for_exploration.pdf\",\"abstract\":\"The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.\",\"booktitle\":\"Advances in Databases and Information Systems\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Chakrabarti\"],\"firstnames\":[\"Arnab\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Das\"],\"firstnames\":[\"Abhijeet\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Quix\"],\"firstnames\":[\"Christoph\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Bellatreche\"],\"firstnames\":[\"Ladjel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dumas\"],\"firstnames\":[\"Marlon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Karras\"],\"firstnames\":[\"Panagiotis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Matulevičius\"],\"firstnames\":[\"Raimundas\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2021\",\"pages\":\"183–197\",\"bibtex\":\"@inproceedings{chakrabarti_unsupervised_2021,\\n\\taddress = {Cham},\\n\\ttitle = {Unsupervised {Feature} {Selection} for {Efficient} {Exploration} of {High} {Dimensional} {Data}},\\n\\tisbn = {978-3-030-82472-3},\\n\\turl = {https://www.cochez.nl/papers/feature_selection_for_exploration.pdf},\\n\\tabstract = {The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.},\\n\\tbooktitle = {Advances in {Databases} and {Information} {Systems}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Chakrabarti, Arnab and Das, Abhijeet and Cochez, Michael and Quix, Christoph},\\n\\teditor = {Bellatreche, Ladjel and Dumas, Marlon and Karras, Panagiotis and Matulevičius, Raimundas},\\n\\tmonth = aug,\\n\\tyear = {2021},\\n\\tpages = {183--197},\\n}\\n\\n\",\"author_short\":[\"Chakrabarti, A.\",\"Das, A.\",\"Cochez, M.\",\"Quix, C.\"],\"editor_short\":[\"Bellatreche, L.\",\"Dumas, M.\",\"Karras, P.\",\"Matulevičius, R.\"],\"key\":\"chakrabarti_unsupervised_2021\",\"id\":\"chakrabarti_unsupervised_2021\",\"bibbaseid\":\"chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.cochez.nl/papers/feature_selection_for_exploration.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging\",\"volume\":\"9\",\"doi\":\"10.1109/ACCESS.2021.3062493\",\"journal\":\"IEEE Access\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Md.\",\"Rezaul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jiao\"],\"firstnames\":[\"Jiao\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Döhmen\"],\"firstnames\":[\"Till\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beyan\"],\"firstnames\":[\"Oya\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rebholz-Schuhmann\"],\"firstnames\":[\"Dietrich\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2021\",\"pages\":\"39757–39780\",\"bibtex\":\"@article{karim_deepkneeexplainer_2021,\\n\\ttitle = {{DeepKneeExplainer}: {Explainable} {Knee} {Osteoarthritis} {Diagnosis} {From} {Radiographs} and {Magnetic} {Resonance} {Imaging}},\\n\\tvolume = {9},\\n\\tdoi = {10.1109/ACCESS.2021.3062493},\\n\\tjournal = {IEEE Access},\\n\\tauthor = {Karim, Md. Rezaul and Jiao, Jiao and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\\n\\tmonth = feb,\\n\\tyear = {2021},\\n\\tpages = {39757--39780},\\n}\\n\\n\",\"author_short\":[\"Karim, M. R.\",\"Jiao, J.\",\"Döhmen, T.\",\"Cochez, M.\",\"Beyan, O.\",\"Rebholz-Schuhmann, D.\",\"Decker, S.\"],\"key\":\"karim_deepkneeexplainer_2021\",\"id\":\"karim_deepkneeexplainer_2021\",\"bibbaseid\":\"karim-jiao-dhmen-cochez-beyan-rebholzschuhmann-decker-deepkneeexplainerexplainablekneeosteoarthritisdiagnosisfromradiographsandmagneticresonanceimaging-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification\",\"isbn\":\"978-3-030-72308-8\",\"abstract\":\"Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.\",\"booktitle\":\"Graph Structures for Knowledge Representation and Reasoning\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[\"van\"],\"lastnames\":[\"Bakel\"],\"firstnames\":[\"Ruud\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Aleksiev\"],\"firstnames\":[\"Teodor\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Croitoru\"],\"firstnames\":[\"Madalina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marquis\"],\"firstnames\":[\"Pierre\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rudolph\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2021\",\"pages\":\"107–124\",\"bibtex\":\"@inproceedings{van_bakel_approximate_2021,\\n\\taddress = {Cham},\\n\\ttitle = {Approximate {Knowledge} {Graph} {Query} {Answering}: {From} {Ranking} to {Binary} {Classification}},\\n\\tisbn = {978-3-030-72308-8},\\n\\tabstract = {Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.},\\n\\tbooktitle = {Graph {Structures} for {Knowledge} {Representation} and {Reasoning}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {van Bakel, Ruud and Aleksiev, Teodor and Daza, Daniel and Alivanistos, Dimitrios and Cochez, Michael},\\n\\teditor = {Cochez, Michael and Croitoru, Madalina and Marquis, Pierre and Rudolph, Sebastian},\\n\\tmonth = sep,\\n\\tyear = {2021},\\n\\tpages = {107--124},\\n}\\n\\n\",\"author_short\":[\"van Bakel, R.\",\"Aleksiev, T.\",\"Daza, D.\",\"Alivanistos, D.\",\"Cochez, M.\"],\"editor_short\":[\"Cochez, M.\",\"Croitoru, M.\",\"Marquis, P.\",\"Rudolph, S.\"],\"key\":\"van_bakel_approximate_2021\",\"id\":\"van_bakel_approximate_2021\",\"bibbaseid\":\"vanbakel-aleksiev-daza-alivanistos-cochez-approximateknowledgegraphqueryansweringfromrankingtobinaryclassification-2021\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"Towards Easy Vocabulary Drafts with Neologism 2.0\",\"isbn\":\"978-3-030-80418-3\",\"url\":\"https://openreview.net/forum?id=PoAI5RrlUFj\",\"abstract\":\"Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.\",\"booktitle\":\"The Semantic Web: ESWC 2021 Satellite Events\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Lipp\"],\"firstnames\":[\"Johannes\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gleim\"],\"firstnames\":[\"Lars\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dimitriadis\"],\"firstnames\":[\"Iraklis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ali\"],\"firstnames\":[\"Hussain\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alvarez\"],\"firstnames\":[\"Daniel\",\"Hoppe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lange\"],\"firstnames\":[\"Christoph\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Verborgh\"],\"firstnames\":[\"Ruben\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dimou\"],\"firstnames\":[\"Anastasia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hogan\"],\"firstnames\":[\"Aidan\"],\"suffixes\":[]},{\"propositions\":[\"d'Amato\"],\"lastnames\":[],\"firstnames\":[\"Claudia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tiddi\"],\"firstnames\":[\"Ilaria\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bröring\"],\"firstnames\":[\"Arne\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mayer\"],\"firstnames\":[\"Simon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ongenae\"],\"firstnames\":[\"Femke\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tommasini\"],\"firstnames\":[\"Riccardo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alam\"],\"firstnames\":[\"Mehwish\"],\"suffixes\":[]}],\"month\":\"June\",\"year\":\"2021\",\"keywords\":\"discoverylab\",\"pages\":\"21–26\",\"bibtex\":\"@inproceedings{lipp_towards_2021,\\n\\taddress = {Cham},\\n\\ttitle = {Towards {Easy} {Vocabulary} {Drafts} with {Neologism} 2.0},\\n\\tisbn = {978-3-030-80418-3},\\n\\turl = {https://openreview.net/forum?id=PoAI5RrlUFj},\\n\\tabstract = {Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.},\\n\\tbooktitle = {The {Semantic} {Web}: {ESWC} 2021 {Satellite} {Events}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Lipp, Johannes and Gleim, Lars and Cochez, Michael and Dimitriadis, Iraklis and Ali, Hussain and Alvarez, Daniel Hoppe and Lange, Christoph and Decker, Stefan},\\n\\teditor = {Verborgh, Ruben and Dimou, Anastasia and Hogan, Aidan and d'Amato, Claudia and Tiddi, Ilaria and Bröring, Arne and Mayer, Simon and Ongenae, Femke and Tommasini, Riccardo and Alam, Mehwish},\\n\\tmonth = jun,\\n\\tyear = {2021},\\n\\tkeywords = {discoverylab},\\n\\tpages = {21--26},\\n}\\n\\n\",\"author_short\":[\"Lipp, J.\",\"Gleim, L.\",\"Cochez, M.\",\"Dimitriadis, I.\",\"Ali, H.\",\"Alvarez, D. H.\",\"Lange, C.\",\"Decker, S.\"],\"editor_short\":[\"Verborgh, R.\",\"Dimou, A.\",\"Hogan, A.\",\"d'Amato , C.\",\"Tiddi, I.\",\"Bröring, A.\",\"Mayer, S.\",\"Ongenae, F.\",\"Tommasini, R.\",\"Alam, M.\"],\"key\":\"lipp_towards_2021\",\"id\":\"lipp_towards_2021\",\"bibbaseid\":\"lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://openreview.net/forum?id=PoAI5RrlUFj\"},\"keyword\":[\"discoverylab\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Hyperbolic Embedding Inference for Structured Multi-Label Prediction\",\"booktitle\":\"NeurIPS2022\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Xiong\"],\"firstnames\":[\"Bo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nayyeri\"],\"firstnames\":[\"Mojtaba\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Staab\"],\"firstnames\":[\"Steffen\"],\"suffixes\":[]}],\"month\":\"November\",\"year\":\"2022\",\"bibtex\":\"@inproceedings{xiong_hyperbolic_2022,\\n\\ttitle = {Hyperbolic {Embedding} {Inference} for {Structured} {Multi}-{Label} {Prediction}},\\n\\tbooktitle = {{NeurIPS2022}},\\n\\tauthor = {Xiong, Bo and Cochez, Michael and Nayyeri, Mojtaba and Staab, Steffen},\\n\\tmonth = nov,\\n\\tyear = {2022},\\n}\\n\\n\",\"author_short\":[\"Xiong, B.\",\"Cochez, M.\",\"Nayyeri, M.\",\"Staab, S.\"],\"key\":\"xiong_hyperbolic_2022\",\"id\":\"xiong_hyperbolic_2022\",\"bibbaseid\":\"xiong-cochez-nayyeri-staab-hyperbolicembeddinginferenceforstructuredmultilabelprediction-2022\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"Question Answering with Additive Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines\",\"volume\":\"13514\",\"url\":\"https://doi.org/10.1007/978-3-031-17105-5\\\\_4\",\"doi\":\"10.1007/978-3-031-17105-5_4\",\"booktitle\":\"Knowledge Engineering and Knowledge Management - 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26-29, 2022, Proceedings\",\"publisher\":\"Springer\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Harper\"],\"firstnames\":[\"Corey\",\"A.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jr\"],\"firstnames\":[\"Ron\",\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Groth\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Corcho\"],\"firstnames\":[\"Óscar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hollink\"],\"firstnames\":[\"Laura\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kutz\"],\"firstnames\":[\"Oliver\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Troquard\"],\"firstnames\":[\"Nicolas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ekaputra\"],\"firstnames\":[\"Fajar\",\"J.\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2022\",\"pages\":\"51–65\",\"bibtex\":\"@inproceedings{harper_question_2022,\\n\\tseries = {Lecture {Notes} in {Computer} {Science}},\\n\\ttitle = {Question {Answering} with {Additive} {Restrictive} {Training} ({QuAART}): {Question} {Answering} for the {Rapid} {Development} of {New} {Knowledge} {Extraction} {Pipelines}},\\n\\tvolume = {13514},\\n\\turl = {https://doi.org/10.1007/978-3-031-17105-5\\\\_4},\\n\\tdoi = {10.1007/978-3-031-17105-5_4},\\n\\tbooktitle = {Knowledge {Engineering} and {Knowledge} {Management} - 23rd {International} {Conference}, {EKAW} 2022, {Bolzano}, {Italy}, {September} 26-29, 2022, {Proceedings}},\\n\\tpublisher = {Springer},\\n\\tauthor = {Harper, Corey A. and Jr, Ron Daniel and Groth, Paul},\\n\\teditor = {Corcho, Óscar and Hollink, Laura and Kutz, Oliver and Troquard, Nicolas and Ekaputra, Fajar J.},\\n\\tmonth = sep,\\n\\tyear = {2022},\\n\\tpages = {51--65},\\n}\\n\\n\",\"author_short\":[\"Harper, C. A.\",\"Jr, R. D.\",\"Groth, P.\"],\"editor_short\":[\"Corcho, Ó.\",\"Hollink, L.\",\"Kutz, O.\",\"Troquard, N.\",\"Ekaputra, F. J.\"],\"key\":\"harper_question_2022\",\"id\":\"harper_question_2022\",\"bibbaseid\":\"harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/978-3-031-17105-5\\\\_4\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"Scientific Item Recommendation Using a Citation Network\",\"isbn\":\"978-3-031-10986-7\",\"booktitle\":\"Knowledge Science, Engineering and Management\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Xu\"],\"suffixes\":[]},{\"propositions\":[\"van\"],\"lastnames\":[\"Harmelen\"],\"firstnames\":[\"Frank\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Huang\"],\"firstnames\":[\"Zhisheng\\\"\"],\"suffixes\":[\"editor=\\\"Memmi\",\",\",\"Gerard\"]},{\"propositions\":[],\"lastnames\":[\"Yang\"],\"firstnames\":[\"Baijian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kong\"],\"firstnames\":[\"Linghe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"Tianwei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Qiu\"],\"firstnames\":[\"Meikang\"],\"suffixes\":[]}],\"month\":\"August\",\"year\":\"2022\",\"pages\":\"469–484\",\"bibtex\":\"@inproceedings{wang_scientific_2022,\\n\\taddress = {Cham},\\n\\ttitle = {Scientific {Item} {Recommendation} {Using} a {Citation} {Network}},\\n\\tisbn = {978-3-031-10986-7},\\n\\tbooktitle = {Knowledge {Science}, {Engineering} and {Management}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Wang, Xu and van Harmelen, Frank and Cochez, Michael and Huang, Zhisheng\\\", editor=\\\"Memmi, Gerard and Yang, Baijian and Kong, Linghe and Zhang, Tianwei and Qiu, Meikang},\\n\\tmonth = aug,\\n\\tyear = {2022},\\n\\tpages = {469--484},\\n}\\n\\n\",\"author_short\":[\"Wang, X.\",\"van Harmelen, F.\",\"Cochez, M.\",\"Huang, Z.\",\"Yang, B.\",\"Kong, L.\",\"Zhang, T.\",\"Qiu, M.\"],\"key\":\"wang_scientific_2022\",\"id\":\"wang_scientific_2022\",\"bibbaseid\":\"wang-vanharmelen-cochez-huang-yang-kong-zhang-qiu-scientificitemrecommendationusingacitationnetwork-2022\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Towards a standard-based open data ecosystem: analysis of DCAT-AP use at national and European level\",\"volume\":\"18\",\"number\":\"2\",\"journal\":\"Electronic Government, an International Journal\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Barthelemy\"],\"firstnames\":[\"Florian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dimitriadis\"],\"firstnames\":[\"Iraklis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Naila\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Loutas\"],\"firstnames\":[\"Nikolaos\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Magnisalis\"],\"firstnames\":[\"Ioannis\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Comet\"],\"firstnames\":[\"Lina\",\"Molinas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Peristeras\"],\"firstnames\":[\"Vassilios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wyns\"],\"firstnames\":[\"Brecht\"],\"suffixes\":[]}],\"month\":\"January\",\"year\":\"2022\",\"note\":\"Publisher: Inderscience Publishers (IEL)\",\"pages\":\"137–180\",\"bibtex\":\"@article{barthelemy_towards_2022,\\n\\ttitle = {Towards a standard-based open data ecosystem: analysis of {DCAT}-{AP} use at national and {European} level},\\n\\tvolume = {18},\\n\\tnumber = {2},\\n\\tjournal = {Electronic Government, an International Journal},\\n\\tauthor = {Barthelemy, Florian and Cochez, Michael and Dimitriadis, Iraklis and Karim, Naila and Loutas, Nikolaos and Magnisalis, Ioannis and Comet, Lina Molinas and Peristeras, Vassilios and Wyns, Brecht},\\n\\tmonth = jan,\\n\\tyear = {2022},\\n\\tnote = {Publisher: Inderscience Publishers (IEL)},\\n\\tpages = {137--180},\\n}\\n\\n\",\"author_short\":[\"Barthelemy, F.\",\"Cochez, M.\",\"Dimitriadis, I.\",\"Karim, N.\",\"Loutas, N.\",\"Magnisalis, I.\",\"Comet, L. M.\",\"Peristeras, V.\",\"Wyns, B.\"],\"key\":\"barthelemy_towards_2022\",\"id\":\"barthelemy_towards_2022\",\"bibbaseid\":\"barthelemy-cochez-dimitriadis-karim-loutas-magnisalis-comet-peristeras-etal-towardsastandardbasedopendataecosystemanalysisofdcatapuseatnationalandeuropeanlevel-2022\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Virtual Event, Lyon, France\",\"series\":\"WWW '22\",\"title\":\"Scaling R-GCN Training with Graph Summarization\",\"isbn\":\"978-1-4503-9130-6\",\"url\":\"https://arxiv.org/abs/2203.02622\",\"doi\":\"https://doi.org/10.1145/3487553.3524719\",\"booktitle\":\"WWW '22: Companion Proceedings of the Web Conference 2022\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Generale\"],\"firstnames\":[\"Alessandro\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Blume\"],\"firstnames\":[\"Till\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2022\",\"note\":\"arXiv preprint arXiv:2203.02622\",\"keywords\":\"graph neural network, graph summarization, scalability\",\"pages\":\"1073–1082\",\"bibtex\":\"@inproceedings{generale_scaling_2022,\\n\\taddress = {Virtual Event, Lyon, France},\\n\\tseries = {{WWW} '22},\\n\\ttitle = {Scaling {R}-{GCN} {Training} with {Graph} {Summarization}},\\n\\tisbn = {978-1-4503-9130-6},\\n\\turl = {https://arxiv.org/abs/2203.02622},\\n\\tdoi = {https://doi.org/10.1145/3487553.3524719},\\n\\tbooktitle = {{WWW} '22: {Companion} {Proceedings} of the {Web} {Conference} 2022},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Generale, Alessandro and Blume, Till and Cochez, Michael},\\n\\tmonth = apr,\\n\\tyear = {2022},\\n\\tnote = {arXiv preprint arXiv:2203.02622},\\n\\tkeywords = {graph neural network, graph summarization, scalability},\\n\\tpages = {1073--1082},\\n}\\n\\n\",\"author_short\":[\"Generale, A.\",\"Blume, T.\",\"Cochez, M.\"],\"key\":\"generale_scaling_2022\",\"id\":\"generale_scaling_2022\",\"bibbaseid\":\"generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2203.02622\"},\"keyword\":[\"graph neural network\",\"graph summarization\",\"scalability\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Exposure-Aware Recommendation using Contextual Bandits\",\"copyright\":\"Creative Commons Attribution 4.0 International\",\"url\":\"https://arxiv.org/abs/2209.01665\",\"doi\":\"10.48550/ARXIV.2209.01665\",\"publisher\":\"5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Mansoury\"],\"firstnames\":[\"Masoud\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mobasher\"],\"firstnames\":[\"Bamshad\"],\"suffixes\":[]},{\"propositions\":[\"van\"],\"lastnames\":[\"Hoof\"],\"firstnames\":[\"Herke\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2022\",\"keywords\":\"FOS: Computer and information sciences, Information Retrieval (cs.IR)\",\"bibtex\":\"@inproceedings{mansoury_exposure-aware_2022,\\n\\ttitle = {Exposure-{Aware} {Recommendation} using {Contextual} {Bandits}},\\n\\tcopyright = {Creative Commons Attribution 4.0 International},\\n\\turl = {https://arxiv.org/abs/2209.01665},\\n\\tdoi = {10.48550/ARXIV.2209.01665},\\n\\tpublisher = {5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022},\\n\\tauthor = {Mansoury, Masoud and Mobasher, Bamshad and van Hoof, Herke},\\n\\tmonth = sep,\\n\\tyear = {2022},\\n\\tkeywords = {FOS: Computer and information sciences, Information Retrieval (cs.IR)},\\n}\\n\\n\",\"author_short\":[\"Mansoury, M.\",\"Mobasher, B.\",\"van Hoof, H.\"],\"key\":\"mansoury_exposure-aware_2022\",\"id\":\"mansoury_exposure-aware_2022\",\"bibbaseid\":\"mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2209.01665\"},\"keyword\":[\"FOS: Computer and information sciences\",\"Information Retrieval (cs.IR)\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Query Embedding on Hyper-Relational Knowledge Graphs\",\"url\":\"https://openreview.net/forum?id=4rLw09TgRw9\",\"booktitle\":\"The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022\",\"publisher\":\"OpenReview.net\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Berrendorf\"],\"firstnames\":[\"Max\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Galkin\"],\"firstnames\":[\"Mikhail\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2022\",\"bibtex\":\"@inproceedings{alivanistos_query_2022,\\n\\ttitle = {Query {Embedding} on {Hyper}-{Relational} {Knowledge} {Graphs}},\\n\\turl = {https://openreview.net/forum?id=4rLw09TgRw9},\\n\\tbooktitle = {The {Tenth} {International} {Conference} on {Learning} {Representations}, {ICLR} 2022, {Virtual} {Event}, {April} 25-29, 2022},\\n\\tpublisher = {OpenReview.net},\\n\\tauthor = {Alivanistos, Dimitrios and Berrendorf, Max and Cochez, Michael and Galkin, Mikhail},\\n\\tmonth = apr,\\n\\tyear = {2022},\\n}\\n\\n\",\"author_short\":[\"Alivanistos, D.\",\"Berrendorf, M.\",\"Cochez, M.\",\"Galkin, M.\"],\"key\":\"alivanistos_query_2022\",\"id\":\"alivanistos_query_2022\",\"bibbaseid\":\"alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://openreview.net/forum?id=4rLw09TgRw9\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"series\":\"CEUR Workshop Proceedings\",\"title\":\"Potential Energy to Improve Link Prediction With Relational Graph Neural Networks\",\"volume\":\"3121\",\"url\":\"http://ceur-ws.org/Vol-3121/short2.pdf\",\"booktitle\":\"Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022\",\"publisher\":\"CEUR-WS.org\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Colombo\"],\"firstnames\":[\"Simone\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Martin\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hinkelmann\"],\"firstnames\":[\"Knut\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fill\"],\"firstnames\":[\"Hans-Georg\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gerber\"],\"firstnames\":[\"Aurona\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lenat\"],\"firstnames\":[\"Doug\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Stolle\"],\"firstnames\":[\"Reinhard\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Harmelen\"],\"firstnames\":[\"Frank\",\"van\"],\"suffixes\":[]}],\"month\":\"March\",\"year\":\"2022\",\"bibtex\":\"@inproceedings{colombo_potential_2022,\\n\\tseries = {{CEUR} {Workshop} {Proceedings}},\\n\\ttitle = {Potential {Energy} to {Improve} {Link} {Prediction} {With} {Relational} {Graph} {Neural} {Networks}},\\n\\tvolume = {3121},\\n\\turl = {http://ceur-ws.org/Vol-3121/short2.pdf},\\n\\tbooktitle = {Proceedings of the {AAAI} 2022 {Spring} {Symposium} on {Machine} {Learning} and {Knowledge} {Engineering} for {Hybrid} {Intelligence} ({AAAI}-{MAKE} 2022), {Stanford} {University}, {Palo} {Alto}, {California}, {USA}, {March} 21-23, 2022},\\n\\tpublisher = {CEUR-WS.org},\\n\\tauthor = {Colombo, Simone and Alivanistos, Dimitrios and Cochez, Michael},\\n\\teditor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and Harmelen, Frank van},\\n\\tmonth = mar,\\n\\tyear = {2022},\\n}\\n\\n\",\"author_short\":[\"Colombo, S.\",\"Alivanistos, D.\",\"Cochez, M.\"],\"editor_short\":[\"Martin, A.\",\"Hinkelmann, K.\",\"Fill, H.\",\"Gerber, A.\",\"Lenat, D.\",\"Stolle, R.\",\"Harmelen, F. v.\"],\"key\":\"colombo_potential_2022\",\"id\":\"colombo_potential_2022\",\"bibbaseid\":\"colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://ceur-ws.org/Vol-3121/short2.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Inductive Entity Representations from Text via Link Prediction\",\"url\":\"https://doi.org/10.1145/3442381.3450141\",\"doi\":\"10.1145/3442381.3450141\",\"booktitle\":\"WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021\",\"publisher\":\"ACM / IW3C2\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Groth\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Leskovec\"],\"firstnames\":[\"Jure\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Grobelnik\"],\"firstnames\":[\"Marko\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Najork\"],\"firstnames\":[\"Marc\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tang\"],\"firstnames\":[\"Jie\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zia\"],\"firstnames\":[\"Leila\"],\"suffixes\":[]}],\"month\":\"April\",\"year\":\"2021\",\"pages\":\"798–808\",\"bibtex\":\"@inproceedings{daza_inductive_2021,\\n\\ttitle = {Inductive {Entity} {Representations} from {Text} via {Link} {Prediction}},\\n\\turl = {https://doi.org/10.1145/3442381.3450141},\\n\\tdoi = {10.1145/3442381.3450141},\\n\\tbooktitle = {{WWW} '21: {The} {Web} {Conference} 2021, {Virtual} {Event} / {Ljubljana}, {Slovenia}, {April} 19-23, 2021},\\n\\tpublisher = {ACM / IW3C2},\\n\\tauthor = {Daza, Daniel and Cochez, Michael and Groth, Paul},\\n\\teditor = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila},\\n\\tmonth = apr,\\n\\tyear = {2021},\\n\\tpages = {798--808},\\n}\\n\\n\",\"author_short\":[\"Daza, D.\",\"Cochez, M.\",\"Groth, P.\"],\"editor_short\":[\"Leskovec, J.\",\"Grobelnik, M.\",\"Najork, M.\",\"Tang, J.\",\"Zia, L.\"],\"key\":\"daza_inductive_2021\",\"id\":\"daza_inductive_2021\",\"bibbaseid\":\"daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1145/3442381.3450141\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Complex Query Answering with Neural Link Predictors\",\"url\":\"https://openreview.net/forum?id=Mos9F9kDwkz\",\"booktitle\":\"International Conference on Learning Representations (ICLR 2021)\",\"publisher\":\"Openreview\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Arakelyan\"],\"firstnames\":[\"Erik\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Minervini\"],\"firstnames\":[\"Pasquale\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2021\",\"note\":\"_eprint: 2011.03459\",\"bibtex\":\"@inproceedings{arakelyan_complex_2021,\\n\\ttitle = {Complex {Query} {Answering} with {Neural} {Link} {Predictors}},\\n\\turl = {https://openreview.net/forum?id=Mos9F9kDwkz},\\n\\tbooktitle = {International {Conference} on {Learning} {Representations} ({ICLR} 2021)},\\n\\tpublisher = {Openreview},\\n\\tauthor = {Arakelyan, Erik and Daza, Daniel and Minervini, Pasquale and Cochez, Michael},\\n\\tmonth = may,\\n\\tyear = {2021},\\n\\tnote = {\\\\_eprint: 2011.03459},\\n}\\n\\n\",\"author_short\":[\"Arakelyan, E.\",\"Daza, D.\",\"Minervini, P.\",\"Cochez, M.\"],\"key\":\"arakelyan_complex_2021\",\"id\":\"arakelyan_complex_2021\",\"bibbaseid\":\"arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://openreview.net/forum?id=Mos9F9kDwkz\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":5,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"series\":\"K-CAP '21\",\"title\":\"Multi-Domain and Explainable Prediction of Changes in Web Vocabularies\",\"isbn\":\"978-1-4503-8457-5\",\"url\":\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583\",\"doi\":\"10.1145/3460210.3493583\",\"abstract\":\"Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.\",\"language\":\"English\",\"booktitle\":\"Proceedings of the 11th on Knowledge Capture Conference\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Meroño-Peñuela\"],\"firstnames\":[\"Albert\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Guéret\"],\"firstnames\":[\"Christophe\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schlobach\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2021\",\"note\":\"event-place: Virtual Event, USA\",\"keywords\":\"change modelling, ontology evolution, vocabulary change\",\"pages\":\"193–200\",\"bibtex\":\"@inproceedings{merono-penuela_multi-domain_2021,\\n\\taddress = {New York, NY, USA},\\n\\tseries = {K-{CAP} '21},\\n\\ttitle = {Multi-{Domain} and {Explainable} {Prediction} of {Changes} in {Web} {Vocabularies}},\\n\\tisbn = {978-1-4503-8457-5},\\n\\turl = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583},\\n\\tdoi = {10.1145/3460210.3493583},\\n\\tabstract = {Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.},\\n\\tlanguage = {English},\\n\\tbooktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Meroño-Peñuela, Albert and Pernisch, Romana and Guéret, Christophe and Schlobach, Stefan},\\n\\tmonth = dec,\\n\\tyear = {2021},\\n\\tnote = {event-place: Virtual Event, USA},\\n\\tkeywords = {change modelling, ontology evolution, vocabulary change},\\n\\tpages = {193--200},\\n}\\n\\n\",\"author_short\":[\"Meroño-Peñuela, A.\",\"Pernisch, R.\",\"Guéret, C.\",\"Schlobach, S.\"],\"key\":\"merono-penuela_multi-domain_2021\",\"id\":\"merono-penuela_multi-domain_2021\",\"bibbaseid\":\"meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583\"},\"keyword\":[\"change modelling\",\"ontology evolution\",\"vocabulary change\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"A Machine With Human-Like Memory Systems\",\"booktitle\":\"arXiv preprint arXiv:2204.01611\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kim\"],\"firstnames\":[\"Taewoon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Francois-Lavet\"],\"firstnames\":[\"Vincent\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neerincx\"],\"firstnames\":[\"Mark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vossen\"],\"firstnames\":[\"Piek\"],\"suffixes\":[]}],\"year\":\"2023\",\"bibtex\":\"@inproceedings{kim_machine_2023,\\n\\ttitle = {A {Machine} {With} {Human}-{Like} {Memory} {Systems}},\\n\\tbooktitle = {{arXiv} preprint {arXiv}:2204.01611},\\n\\tauthor = {Kim, Taewoon and Cochez, Michael and Francois-Lavet, Vincent and Neerincx, Mark and Vossen, Piek},\\n\\tyear = {2023},\\n}\\n\\n\",\"author_short\":[\"Kim, T.\",\"Cochez, M.\",\"Francois-Lavet, V.\",\"Neerincx, M.\",\"Vossen, P.\"],\"key\":\"kim_machine_2023\",\"id\":\"kim_machine_2023\",\"bibbaseid\":\"kim-cochez-francoislavet-neerincx-vossen-amachinewithhumanlikememorysystems-2023\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Prompting as Probing: Using Language Models for Knowledge Base Construction\",\"volume\":\"3274\",\"copyright\":\"arXiv.org perpetual, non-exclusive license\",\"url\":\"https://ceur-ws.org/Vol-3274/paper2.pdf\",\"doi\":\"10.48550/ARXIV.2208.11057\",\"booktitle\":\"Proceedings of the Semantic Web Challenge on Knowledge Base Construction from Pre-trained Language Models 2022 co-located with the 21st International Semantic Web Conference (ISWC2022)\",\"publisher\":\"CEUR-ws.org\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Alivanistos\"],\"firstnames\":[\"Dimitrios\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Santamaría\"],\"firstnames\":[\"Selene\",\"Báez\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kalo\"],\"firstnames\":[\"Jan-Christoph\"],\"suffixes\":[]},{\"propositions\":[\"van\"],\"lastnames\":[\"Krieken\"],\"firstnames\":[\"Emile\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Thanapalasingam\"],\"firstnames\":[\"Thiviyan\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2022\",\"keywords\":\"Artificial Intelligence (cs.AI), Computation and Language (cs.CL), FOS: Computer and information sciences\",\"pages\":\"11–34\",\"bibtex\":\"@inproceedings{alivanistos_prompting_2022,\\n\\ttitle = {Prompting as {Probing}: {Using} {Language} {Models} for {Knowledge} {Base} {Construction}},\\n\\tvolume = {3274},\\n\\tcopyright = {arXiv.org perpetual, non-exclusive license},\\n\\turl = {https://ceur-ws.org/Vol-3274/paper2.pdf},\\n\\tdoi = {10.48550/ARXIV.2208.11057},\\n\\tbooktitle = {Proceedings of the {Semantic} {Web} {Challenge} on {Knowledge} {Base} {Construction} from {Pre}-trained {Language} {Models} 2022 co-located with the 21st {International} {Semantic} {Web} {Conference} ({ISWC2022})},\\n\\tpublisher = {CEUR-ws.org},\\n\\tauthor = {Alivanistos, Dimitrios and Santamaría, Selene Báez and Cochez, Michael and Kalo, Jan-Christoph and van Krieken, Emile and Thanapalasingam, Thiviyan},\\n\\tmonth = oct,\\n\\tyear = {2022},\\n\\tkeywords = {Artificial Intelligence (cs.AI), Computation and Language (cs.CL), FOS: Computer and information sciences},\\n\\tpages = {11--34},\\n}\\n\\n\",\"author_short\":[\"Alivanistos, D.\",\"Santamaría, S. B.\",\"Cochez, M.\",\"Kalo, J.\",\"van Krieken, E.\",\"Thanapalasingam, T.\"],\"key\":\"alivanistos_prompting_2022\",\"id\":\"alivanistos_prompting_2022\",\"bibbaseid\":\"alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://ceur-ws.org/Vol-3274/paper2.pdf\"},\"keyword\":[\"Artificial Intelligence (cs.AI)\",\"Computation and Language (cs.CL)\",\"FOS: Computer and information sciences\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":4,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"Parameter-Efficient Sparse Retrievers and Rerankers Using Adapters\",\"isbn\":\"978-3-031-28238-6\",\"doi\":\"10.1007/978-3-031-28238-6_2\",\"abstract\":\"Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade.)\",\"language\":\"en\",\"booktitle\":\"Advances in Information Retrieval\",\"publisher\":\"Springer Nature Switzerland\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pal\"],\"firstnames\":[\"Vaishali\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lassance\"],\"firstnames\":[\"Carlos\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Déjean\"],\"firstnames\":[\"Hervé\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Clinchant\"],\"firstnames\":[\"Stéphane\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Kamps\"],\"firstnames\":[\"Jaap\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Goeuriot\"],\"firstnames\":[\"Lorraine\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Crestani\"],\"firstnames\":[\"Fabio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Maistro\"],\"firstnames\":[\"Maria\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Joho\"],\"firstnames\":[\"Hideo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Davis\"],\"firstnames\":[\"Brian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gurrin\"],\"firstnames\":[\"Cathal\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kruschwitz\"],\"firstnames\":[\"Udo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Caputo\"],\"firstnames\":[\"Annalina\"],\"suffixes\":[]}],\"year\":\"2023\",\"keywords\":\"Adapters, Information Retrieval, Sparse neural retriever\",\"pages\":\"16–31\",\"bibtex\":\"@inproceedings{pal_parameter-efficient_2023,\\n\\taddress = {Cham},\\n\\tseries = {Lecture {Notes} in {Computer} {Science}},\\n\\ttitle = {Parameter-{Efficient} {Sparse} {Retrievers} and {Rerankers} {Using} {Adapters}},\\n\\tisbn = {978-3-031-28238-6},\\n\\tdoi = {10.1007/978-3-031-28238-6_2},\\n\\tabstract = {Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2\\\\% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade.)},\\n\\tlanguage = {en},\\n\\tbooktitle = {Advances in {Information} {Retrieval}},\\n\\tpublisher = {Springer Nature Switzerland},\\n\\tauthor = {Pal, Vaishali and Lassance, Carlos and Déjean, Hervé and Clinchant, Stéphane},\\n\\teditor = {Kamps, Jaap and Goeuriot, Lorraine and Crestani, Fabio and Maistro, Maria and Joho, Hideo and Davis, Brian and Gurrin, Cathal and Kruschwitz, Udo and Caputo, Annalina},\\n\\tyear = {2023},\\n\\tkeywords = {Adapters, Information Retrieval, Sparse neural retriever},\\n\\tpages = {16--31},\\n}\\n\\n\",\"author_short\":[\"Pal, V.\",\"Lassance, C.\",\"Déjean, H.\",\"Clinchant, S.\"],\"editor_short\":[\"Kamps, J.\",\"Goeuriot, L.\",\"Crestani, F.\",\"Maistro, M.\",\"Joho, H.\",\"Davis, B.\",\"Gurrin, C.\",\"Kruschwitz, U.\",\"Caputo, A.\"],\"key\":\"pal_parameter-efficient_2023\",\"id\":\"pal_parameter-efficient_2023\",\"bibbaseid\":\"pal-lassance-djean-clinchant-parameterefficientsparseretrieversandrerankersusingadapters-2023\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Adapters\",\"Information Retrieval\",\"Sparse neural retriever\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"New York, NY, USA\",\"series\":\"SIGIR '22\",\"title\":\"The Seventh Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI'22)\",\"isbn\":\"978-1-4503-8732-3\",\"url\":\"https://dl.acm.org/doi/10.1145/3477495.3531700\",\"doi\":\"10.1145/3477495.3531700\",\"abstract\":\"The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.\",\"urldate\":\"2023-04-20\",\"booktitle\":\"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval\",\"publisher\":\"Association for Computing Machinery\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Penha\"],\"firstnames\":[\"Gustavo\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Vakulenko\"],\"firstnames\":[\"Svitlana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dusek\"],\"firstnames\":[\"Ondrej\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Clark\"],\"firstnames\":[\"Leigh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Pal\"],\"firstnames\":[\"Vaishali\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Adlakha\"],\"firstnames\":[\"Vaibhav\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2022\",\"keywords\":\"conversational information access, conversational search, information-seeking dialogue\",\"pages\":\"3466–3469\",\"bibtex\":\"@inproceedings{penha_seventh_2022,\\n\\taddress = {New York, NY, USA},\\n\\tseries = {{SIGIR} '22},\\n\\ttitle = {The {Seventh} {Workshop} on {Search}-{Oriented} {Conversational} {Artificial} {Intelligence} ({SCAI}'22)},\\n\\tisbn = {978-1-4503-8732-3},\\n\\turl = {https://dl.acm.org/doi/10.1145/3477495.3531700},\\n\\tdoi = {10.1145/3477495.3531700},\\n\\tabstract = {The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.},\\n\\turldate = {2023-04-20},\\n\\tbooktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},\\n\\tpublisher = {Association for Computing Machinery},\\n\\tauthor = {Penha, Gustavo and Vakulenko, Svitlana and Dusek, Ondrej and Clark, Leigh and Pal, Vaishali and Adlakha, Vaibhav},\\n\\tmonth = jul,\\n\\tyear = {2022},\\n\\tkeywords = {conversational information access, conversational search, information-seeking dialogue},\\n\\tpages = {3466--3469},\\n}\\n\\n\",\"author_short\":[\"Penha, G.\",\"Vakulenko, S.\",\"Dusek, O.\",\"Clark, L.\",\"Pal, V.\",\"Adlakha, V.\"],\"key\":\"penha_seventh_2022\",\"id\":\"penha_seventh_2022\",\"bibbaseid\":\"penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3477495.3531700\"},\"keyword\":[\"conversational information access\",\"conversational search\",\"information-seeking dialogue\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Amsterdam Netherlands\",\"title\":\"MORS 2021: 1st Workshop on Multi-Objective Recommender Systems\",\"isbn\":\"978-1-4503-8458-2\",\"shorttitle\":\"MORS 2021\",\"url\":\"https://dl.acm.org/doi/10.1145/3460231.3470936\",\"doi\":\"10.1145/3460231.3470936\",\"language\":\"en\",\"urldate\":\"2023-04-20\",\"booktitle\":\"Fifteenth ACM Conference on Recommender Systems\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Abdollahpouri\"],\"firstnames\":[\"Himan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Elahi\"],\"firstnames\":[\"Mehdi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mansoury\"],\"firstnames\":[\"Masoud\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sahebi\"],\"firstnames\":[\"Shaghayegh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nazari\"],\"firstnames\":[\"Zahra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Chaney\"],\"firstnames\":[\"Allison\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Loni\"],\"firstnames\":[\"Babak\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2021\",\"pages\":\"787–788\",\"bibtex\":\"@inproceedings{abdollahpouri_mors_2021,\\n\\taddress = {Amsterdam Netherlands},\\n\\ttitle = {{MORS} 2021: 1st {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},\\n\\tisbn = {978-1-4503-8458-2},\\n\\tshorttitle = {{MORS} 2021},\\n\\turl = {https://dl.acm.org/doi/10.1145/3460231.3470936},\\n\\tdoi = {10.1145/3460231.3470936},\\n\\tlanguage = {en},\\n\\turldate = {2023-04-20},\\n\\tbooktitle = {Fifteenth {ACM} {Conference} on {Recommender} {Systems}},\\n\\tpublisher = {ACM},\\n\\tauthor = {Abdollahpouri, Himan and Elahi, Mehdi and Mansoury, Masoud and Sahebi, Shaghayegh and Nazari, Zahra and Chaney, Allison and Loni, Babak},\\n\\tmonth = sep,\\n\\tyear = {2021},\\n\\tpages = {787--788},\\n}\\n\\n\",\"author_short\":[\"Abdollahpouri, H.\",\"Elahi, M.\",\"Mansoury, M.\",\"Sahebi, S.\",\"Nazari, Z.\",\"Chaney, A.\",\"Loni, B.\"],\"key\":\"abdollahpouri_mors_2021\",\"id\":\"abdollahpouri_mors_2021\",\"bibbaseid\":\"abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3460231.3470936\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Knowledge Graphs\",\"volume\":\"54\",\"issn\":\"0360-0300\",\"url\":\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772\",\"doi\":\"10.1145/3447772\",\"abstract\":\"In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.\",\"number\":\"4\",\"journal\":\"ACM Comput. Surv.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Hogan\"],\"firstnames\":[\"Aidan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Blomqvist\"],\"firstnames\":[\"Eva\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"D’amato\"],\"firstnames\":[\"Claudia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Melo\"],\"firstnames\":[\"Gerard\",\"De\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gutierrez\"],\"firstnames\":[\"Claudio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirrane\"],\"firstnames\":[\"Sabrina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gayo\"],\"firstnames\":[\"José\",\"Emilio\",\"Labra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Navigli\"],\"firstnames\":[\"Roberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neumaier\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ngomo\"],\"firstnames\":[\"Axel-Cyrille\",\"Ngonga\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Polleres\"],\"firstnames\":[\"Axel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rashid\"],\"firstnames\":[\"Sabbir\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rula\"],\"firstnames\":[\"Anisa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schmelzeisen\"],\"firstnames\":[\"Lukas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sequeda\"],\"firstnames\":[\"Juan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Staab\"],\"firstnames\":[\"Steffen\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zimmermann\"],\"firstnames\":[\"Antoine\"],\"suffixes\":[]}],\"month\":\"July\",\"year\":\"2021\",\"note\":\"Place: New York, NY, USA Publisher: Association for Computing Machinery\",\"keywords\":\"Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes\",\"bibtex\":\"@article{hogan_knowledge_2021,\\n\\ttitle = {Knowledge {Graphs}},\\n\\tvolume = {54},\\n\\tissn = {0360-0300},\\n\\turl = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772},\\n\\tdoi = {10.1145/3447772},\\n\\tabstract = {In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.},\\n\\tnumber = {4},\\n\\tjournal = {ACM Comput. Surv.},\\n\\tauthor = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and D’amato, Claudia and Melo, Gerard De and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and Ngomo, Axel-Cyrille Ngonga and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},\\n\\tmonth = jul,\\n\\tyear = {2021},\\n\\tnote = {Place: New York, NY, USA\\nPublisher: Association for Computing Machinery},\\n\\tkeywords = {Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes},\\n}\\n\\n\",\"author_short\":[\"Hogan, A.\",\"Blomqvist, E.\",\"Cochez, M.\",\"D’amato, C.\",\"Melo, G. D.\",\"Gutierrez, C.\",\"Kirrane, S.\",\"Gayo, J. E. L.\",\"Navigli, R.\",\"Neumaier, S.\",\"Ngomo, A. N.\",\"Polleres, A.\",\"Rashid, S. M.\",\"Rula, A.\",\"Schmelzeisen, L.\",\"Sequeda, J.\",\"Staab, S.\",\"Zimmermann, A.\"],\"key\":\"hogan_knowledge_2021\",\"id\":\"hogan_knowledge_2021\",\"bibbaseid\":\"hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772\"},\"keyword\":[\"Knowledge graphs\",\"embeddings\",\"graph algorithms\",\"graph databases\",\"graph neural networks\",\"graph query languages\",\"ontologies\",\"rule mining\",\"shapes\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":13,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Seattle WA USA\",\"title\":\"MORS 2022: The Second Workshop on Multi-Objective Recommender Systems\",\"isbn\":\"978-1-4503-9278-5\",\"shorttitle\":\"MORS 2022\",\"url\":\"https://dl.acm.org/doi/10.1145/3523227.3547410\",\"doi\":\"10.1145/3523227.3547410\",\"language\":\"en\",\"urldate\":\"2023-04-20\",\"booktitle\":\"Sixteenth ACM Conference on Recommender Systems\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Abdollahpouri\"],\"firstnames\":[\"Himan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sahebi\"],\"firstnames\":[\"Shaghayegh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Elahi\"],\"firstnames\":[\"Mehdi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mansoury\"],\"firstnames\":[\"Masoud\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Loni\"],\"firstnames\":[\"Babak\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nazari\"],\"firstnames\":[\"Zahra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dimakopoulou\"],\"firstnames\":[\"Maria\"],\"suffixes\":[]}],\"month\":\"September\",\"year\":\"2022\",\"pages\":\"658–660\",\"bibtex\":\"@inproceedings{abdollahpouri_mors_2022,\\n\\taddress = {Seattle WA USA},\\n\\ttitle = {{MORS} 2022: {The} {Second} {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},\\n\\tisbn = {978-1-4503-9278-5},\\n\\tshorttitle = {{MORS} 2022},\\n\\turl = {https://dl.acm.org/doi/10.1145/3523227.3547410},\\n\\tdoi = {10.1145/3523227.3547410},\\n\\tlanguage = {en},\\n\\turldate = {2023-04-20},\\n\\tbooktitle = {Sixteenth {ACM} {Conference} on {Recommender} {Systems}},\\n\\tpublisher = {ACM},\\n\\tauthor = {Abdollahpouri, Himan and Sahebi, Shaghayegh and Elahi, Mehdi and Mansoury, Masoud and Loni, Babak and Nazari, Zahra and Dimakopoulou, Maria},\\n\\tmonth = sep,\\n\\tyear = {2022},\\n\\tpages = {658--660},\\n}\\n\\n\",\"author_short\":[\"Abdollahpouri, H.\",\"Sahebi, S.\",\"Elahi, M.\",\"Mansoury, M.\",\"Loni, B.\",\"Nazari, Z.\",\"Dimakopoulou, M.\"],\"key\":\"abdollahpouri_mors_2022\",\"id\":\"abdollahpouri_mors_2022\",\"bibbaseid\":\"abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3523227.3547410\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Dublin, Ireland\",\"title\":\"SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning\",\"url\":\"https://aclanthology.org/2022.spnlp-1.4\",\"doi\":\"10.18653/v1/2022.spnlp-1.4\",\"abstract\":\"We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95% of the precision of a supervised method, and 84% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.\",\"booktitle\":\"Proceedings of the Sixth Workshop on Structured Prediction for NLP\",\"publisher\":\"Association for Computational Linguistics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Groth\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2022\",\"pages\":\"32–39\",\"bibtex\":\"@inproceedings{daza_slotgan_2022,\\n\\taddress = {Dublin, Ireland},\\n\\ttitle = {{SlotGAN}: {Detecting} {Mentions} in {Text} via {Adversarial} {Distant} {Learning}},\\n\\turl = {https://aclanthology.org/2022.spnlp-1.4},\\n\\tdoi = {10.18653/v1/2022.spnlp-1.4},\\n\\tabstract = {We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95\\\\% of the precision of a supervised method, and 84\\\\% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.},\\n\\tbooktitle = {Proceedings of the {Sixth} {Workshop} on {Structured} {Prediction} for {NLP}},\\n\\tpublisher = {Association for Computational Linguistics},\\n\\tauthor = {Daza, Daniel and Cochez, Michael and Groth, Paul},\\n\\tmonth = may,\\n\\tyear = {2022},\\n\\tpages = {32--39},\\n}\\n\\n\",\"author_short\":[\"Daza, D.\",\"Cochez, M.\",\"Groth, P.\"],\"key\":\"daza_slotgan_2022\",\"id\":\"daza_slotgan_2022\",\"bibbaseid\":\"daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://aclanthology.org/2022.spnlp-1.4\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images\",\"url\":\"https://arxiv.org/abs/2004.04582\",\"doi\":\"10.1109/BIBM49941.2020.9313304\",\"booktitle\":\"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Md.\",\"Rezaul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Döhmen\"],\"firstnames\":[\"Till\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beyan\"],\"firstnames\":[\"Oya\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rebholz-Schuhmann\"],\"firstnames\":[\"Dietrich\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"year\":\"2020\",\"pages\":\"1034–1037\",\"bibtex\":\"@inproceedings{karim_deepcovidexplainer_2020,\\n\\ttitle = {{DeepCOVIDExplainer}: {Explainable} {COVID}-19 {Diagnosis} from {Chest} {X}-ray {Images}},\\n\\turl = {https://arxiv.org/abs/2004.04582},\\n\\tdoi = {10.1109/BIBM49941.2020.9313304},\\n\\tbooktitle = {2020 {IEEE} {International} {Conference} on {Bioinformatics} and {Biomedicine} ({BIBM})},\\n\\tauthor = {Karim, Md. Rezaul and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\\n\\tyear = {2020},\\n\\tpages = {1034--1037},\\n}\\n\\n\",\"author_short\":[\"Karim, M. R.\",\"Döhmen, T.\",\"Cochez, M.\",\"Beyan, O.\",\"Rebholz-Schuhmann, D.\",\"Decker, S.\"],\"key\":\"karim_deepcovidexplainer_2020\",\"id\":\"karim_deepcovidexplainer_2020\",\"bibbaseid\":\"karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2004.04582\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutifonal-LSTM Network\",\"url\":\"https://arxiv.org/abs/2004.07807\",\"doi\":\"10.1109/DSAA49011.2020.00053\",\"booktitle\":\"2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Md.\",\"Rezaul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Raja\",\"Chakravarthi\"],\"firstnames\":[\"Bharathi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"McCrae\"],\"firstnames\":[\"John\",\"P.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"year\":\"2020\",\"pages\":\"390–399\",\"bibtex\":\"@inproceedings{karim_classification_2020,\\n\\ttitle = {Classification {Benchmarks} for {Under}-resourced {Bengali} {Language} based on {Multichannel} {Convolutifonal}-{LSTM} {Network}},\\n\\turl = {https://arxiv.org/abs/2004.07807},\\n\\tdoi = {10.1109/DSAA49011.2020.00053},\\n\\tbooktitle = {2020 {IEEE} 7th {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})},\\n\\tauthor = {Karim, Md. Rezaul and Raja Chakravarthi, Bharathi and McCrae, John P. and Cochez, Michael},\\n\\tyear = {2020},\\n\\tpages = {390--399},\\n}\\n\\n\",\"author_short\":[\"Karim, M. R.\",\"Raja Chakravarthi, B.\",\"McCrae, J. P.\",\"Cochez, M.\"],\"key\":\"karim_classification_2020\",\"id\":\"karim_classification_2020\",\"bibbaseid\":\"karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2004.07807\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Cham\",\"title\":\"GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques\",\"isbn\":\"978-3-030-49461-2\",\"abstract\":\"While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.\",\"booktitle\":\"The Semantic Web\",\"publisher\":\"Springer International Publishing\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pellegrino\"],\"firstnames\":[\"Maria\",\"Angela\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Altabba\"],\"firstnames\":[\"Abdulrahman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Garofalo\"],\"firstnames\":[\"Martina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ristoski\"],\"firstnames\":[\"Petar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[],\"lastnames\":[\"Harth\"],\"firstnames\":[\"Andreas\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirrane\"],\"firstnames\":[\"Sabrina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ngonga\",\"Ngomo\"],\"firstnames\":[\"Axel-Cyrille\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Paulheim\"],\"firstnames\":[\"Heiko\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rula\"],\"firstnames\":[\"Anisa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gentile\"],\"firstnames\":[\"Anna\",\"Lisa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Haase\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"year\":\"2020\",\"pages\":\"565–582\",\"bibtex\":\"@inproceedings{pellegrino_geval_2020,\\n\\taddress = {Cham},\\n\\ttitle = {{GEval}: {A} {Modular} and {Extensible} {Evaluation} {Framework} for {Graph} {Embedding} {Techniques}},\\n\\tisbn = {978-3-030-49461-2},\\n\\tabstract = {While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.},\\n\\tbooktitle = {The {Semantic} {Web}},\\n\\tpublisher = {Springer International Publishing},\\n\\tauthor = {Pellegrino, Maria Angela and Altabba, Abdulrahman and Garofalo, Martina and Ristoski, Petar and Cochez, Michael},\\n\\teditor = {Harth, Andreas and Kirrane, Sabrina and Ngonga Ngomo, Axel-Cyrille and Paulheim, Heiko and Rula, Anisa and Gentile, Anna Lisa and Haase, Peter and Cochez, Michael},\\n\\tyear = {2020},\\n\\tpages = {565--582},\\n}\\n\\n\",\"author_short\":[\"Pellegrino, M. A.\",\"Altabba, A.\",\"Garofalo, M.\",\"Ristoski, P.\",\"Cochez, M.\"],\"editor_short\":[\"Harth, A.\",\"Kirrane, S.\",\"Ngonga Ngomo, A.\",\"Paulheim, H.\",\"Rula, A.\",\"Gentile, A. L.\",\"Haase, P.\",\"Cochez, M.\"],\"key\":\"pellegrino_geval_2020\",\"id\":\"pellegrino_geval_2020\",\"bibbaseid\":\"pellegrino-altabba-garofalo-ristoski-cochez-gevalamodularandextensibleevaluationframeworkforgraphembeddingtechniques-2020\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Deep learning-based clustering approaches for bioinformatics\",\"issn\":\"1477-4054\",\"url\":\"https://doi.org/10.1093/bib/bbz170\",\"doi\":\"10.1093/bib/bbz170\",\"abstract\":\"Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.\",\"journal\":\"Briefings in Bioinformatics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Karim\"],\"firstnames\":[\"Md\",\"Rezaul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Beyan\"],\"firstnames\":[\"Oya\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zappa\"],\"firstnames\":[\"Achille\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Costa\"],\"firstnames\":[\"Ivan\",\"G\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rebholz-Schuhmann\"],\"firstnames\":[\"Dietrich\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"February\",\"year\":\"2020\",\"note\":\"_eprint: https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbz170/32313172/bbz170.pdf\",\"bibtex\":\"@article{karim_deep_2020,\\n\\ttitle = {Deep learning-based clustering approaches for bioinformatics},\\n\\tissn = {1477-4054},\\n\\turl = {https://doi.org/10.1093/bib/bbz170},\\n\\tdoi = {10.1093/bib/bbz170},\\n\\tabstract = {Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.},\\n\\tjournal = {Briefings in Bioinformatics},\\n\\tauthor = {Karim, Md Rezaul and Beyan, Oya and Zappa, Achille and Costa, Ivan G and Rebholz-Schuhmann, Dietrich and Cochez, Michael and Decker, Stefan},\\n\\tmonth = feb,\\n\\tyear = {2020},\\n\\tnote = {\\\\_eprint: https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbz170/32313172/bbz170.pdf},\\n}\\n\\n\",\"author_short\":[\"Karim, M. R.\",\"Beyan, O.\",\"Zappa, A.\",\"Costa, I. G\",\"Rebholz-Schuhmann, D.\",\"Cochez, M.\",\"Decker, S.\"],\"key\":\"karim_deep_2020\",\"id\":\"karim_deep_2020\",\"bibbaseid\":\"karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1093/bib/bbz170\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Structured query construction via knowledge graph embedding\",\"volume\":\"62\",\"issn\":\"0219-3116\",\"url\":\"https://doi.org/10.1007/s10115-019-01401-x\",\"doi\":\"10.1007/s10115-019-01401-x\",\"abstract\":\"In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.\",\"number\":\"5\",\"journal\":\"Knowledge and Information Systems\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Ruijie\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Wang\"],\"firstnames\":[\"Meng\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Liu\"],\"firstnames\":[\"Jun\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Decker\"],\"firstnames\":[\"Stefan\"],\"suffixes\":[]}],\"month\":\"May\",\"year\":\"2020\",\"pages\":\"1819–1846\",\"bibtex\":\"@article{wang_structured_2020,\\n\\ttitle = {Structured query construction via knowledge graph embedding},\\n\\tvolume = {62},\\n\\tissn = {0219-3116},\\n\\turl = {https://doi.org/10.1007/s10115-019-01401-x},\\n\\tdoi = {10.1007/s10115-019-01401-x},\\n\\tabstract = {In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.},\\n\\tnumber = {5},\\n\\tjournal = {Knowledge and Information Systems},\\n\\tauthor = {Wang, Ruijie and Wang, Meng and Liu, Jun and Cochez, Michael and Decker, Stefan},\\n\\tmonth = may,\\n\\tyear = {2020},\\n\\tpages = {1819--1846},\\n}\\n\\n\",\"author_short\":[\"Wang, R.\",\"Wang, M.\",\"Liu, J.\",\"Cochez, M.\",\"Decker, S.\"],\"key\":\"wang_structured_2020\",\"id\":\"wang_structured_2020\",\"bibbaseid\":\"wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://doi.org/10.1007/s10115-019-01401-x\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"book\",\"type\":\"book\",\"series\":\"Synthesis Lectures on Data, Semantics, and Knowledge\",\"title\":\"Knowledge graphs\",\"volume\":\"12\",\"isbn\":\"978-3-031-01918-0\",\"url\":\"https://kgbook.org\",\"publisher\":\"Springer (formerly Morgan & Claypool Publishers)\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Hogan\"],\"firstnames\":[\"Aidan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Blomqvist\"],\"firstnames\":[\"Eva\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]},{\"propositions\":[\"d'Amato\"],\"lastnames\":[],\"firstnames\":[\"Claudia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Melo\"],\"firstnames\":[\"Gerard\",\"de\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gutierrez\"],\"firstnames\":[\"Claudio\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kirrane\"],\"firstnames\":[\"Sabrina\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gayo\"],\"firstnames\":[\"José\",\"Emilio\",\"Labra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Navigli\"],\"firstnames\":[\"Roberto\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neumaier\"],\"firstnames\":[\"Sebastian\"],\"suffixes\":[]},{\"firstnames\":[],\"propositions\":[],\"lastnames\":[\"others\"],\"suffixes\":[]}],\"year\":\"2021\",\"doi\":\"10.2200/S01125ED1V01Y202109DSK022\",\"bibtex\":\"@book{hogan_knowledge_2021-1,\\n\\tseries = {Synthesis {Lectures} on {Data}, {Semantics}, and {Knowledge}},\\n\\ttitle = {Knowledge graphs},\\n\\tvolume = {12},\\n\\tisbn = {978-3-031-01918-0},\\n\\turl = {https://kgbook.org},\\n\\tpublisher = {Springer (formerly Morgan \\\\& Claypool Publishers)},\\n\\tauthor = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and Melo, Gerard de and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and {others}},\\n\\tyear = {2021},\\n\\tdoi = {10.2200/S01125ED1V01Y202109DSK022},\\n}\\n\\n\",\"author_short\":[\"Hogan, A.\",\"Blomqvist, E.\",\"Cochez, M.\",\"d'Amato , C.\",\"Melo, G. d.\",\"Gutierrez, C.\",\"Kirrane, S.\",\"Gayo, J. E. L.\",\"Navigli, R.\",\"Neumaier, S.\",\"others\"],\"key\":\"hogan_knowledge_2021-1\",\"id\":\"hogan_knowledge_2021-1\",\"bibbaseid\":\"hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://kgbook.org\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":13,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Message Passing Query Embedding\",\"url\":\"https://arxiv.org/abs/2002.02406\",\"booktitle\":\"ICML Workshop - Graph Representation Learning and Beyond\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Daza\"],\"firstnames\":[\"Daniel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cochez\"],\"firstnames\":[\"Michael\"],\"suffixes\":[]}],\"year\":\"2020\",\"bibtex\":\"@inproceedings{daza_message_2020,\\n\\ttitle = {Message {Passing} {Query} {Embedding}},\\n\\turl = {https://arxiv.org/abs/2002.02406},\\n\\tbooktitle = {{ICML} {Workshop} - {Graph} {Representation} {Learning} and {Beyond}},\\n\\tauthor = {Daza, Daniel and Cochez, Michael},\\n\\tyear = {2020},\\n}\\n\\n\",\"author_short\":[\"Daza, D.\",\"Cochez, M.\"],\"key\":\"daza_message_2020\",\"id\":\"daza_message_2020\",\"bibbaseid\":\"daza-cochez-messagepassingqueryembedding-2020\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://arxiv.org/abs/2002.02406\"},\"metadata\":{\"authorlinks\":{\"daza, d\":\"https://discoverylab.ai/\"}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Visualising the effects of ontology changes and studying their understanding with ChImp\",\"volume\":\"74\",\"issn\":\"1570-8268\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S1570826822000117\",\"doi\":\"10.1016/j.websem.2022.100715\",\"abstract\":\"Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.\",\"language\":\"en\",\"urldate\":\"2022-06-20\",\"journal\":\"Journal of Web Semantics\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dell’Aglio\"],\"firstnames\":[\"Daniele\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Serbak\"],\"firstnames\":[\"Mirko\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gonçalves\"],\"firstnames\":[\"Rafael\",\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bernstein\"],\"firstnames\":[\"Abraham\"],\"suffixes\":[]}],\"month\":\"October\",\"year\":\"2022\",\"keywords\":\"Materialisation, Ontology editing, Ontology evolution impact, User study\",\"pages\":\"100715\",\"bibtex\":\"@article{pernisch_visualising_2022,\\n\\ttitle = {Visualising the effects of ontology changes and studying their understanding with {ChImp}},\\n\\tvolume = {74},\\n\\tissn = {1570-8268},\\n\\turl = {https://www.sciencedirect.com/science/article/pii/S1570826822000117},\\n\\tdoi = {10.1016/j.websem.2022.100715},\\n\\tabstract = {Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.},\\n\\tlanguage = {en},\\n\\turldate = {2022-06-20},\\n\\tjournal = {Journal of Web Semantics},\\n\\tauthor = {Pernisch, Romana and Dell’Aglio, Daniele and Serbak, Mirko and Gonçalves, Rafael S. and Bernstein, Abraham},\\n\\tmonth = oct,\\n\\tyear = {2022},\\n\\tkeywords = {Materialisation, Ontology editing, Ontology evolution impact, User study},\\n\\tpages = {100715},\\n}\\n\\n\",\"author_short\":[\"Pernisch, R.\",\"Dell’Aglio, D.\",\"Serbak, M.\",\"Gonçalves, R. S.\",\"Bernstein, A.\"],\"key\":\"pernisch_visualising_2022\",\"id\":\"pernisch_visualising_2022\",\"bibbaseid\":\"pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S1570826822000117\"},\"keyword\":[\"Materialisation\",\"Ontology editing\",\"Ontology evolution impact\",\"User study\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":7,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"address\":\"Virtual Event USA\",\"title\":\"Toward Measuring the Resemblance of Embedding Models for Evolving Ontologies\",\"isbn\":\"978-1-4503-8457-5\",\"url\":\"https://dl.acm.org/doi/10.1145/3460210.3493540\",\"doi\":\"10.1145/3460210.3493540\",\"language\":\"en\",\"urldate\":\"2021-12-08\",\"booktitle\":\"Proceedings of the 11th on Knowledge Capture Conference\",\"publisher\":\"ACM\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Pernisch\"],\"firstnames\":[\"Romana\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dell'Aglio\"],\"firstnames\":[\"Daniele\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bernstein\"],\"firstnames\":[\"Abraham\"],\"suffixes\":[]}],\"month\":\"December\",\"year\":\"2021\",\"pages\":\"177–184\",\"bibtex\":\"@inproceedings{pernisch_toward_2021,\\n\\taddress = {Virtual Event USA},\\n\\ttitle = {Toward {Measuring} the {Resemblance} of {Embedding} {Models} for {Evolving} {Ontologies}},\\n\\tisbn = {978-1-4503-8457-5},\\n\\turl = {https://dl.acm.org/doi/10.1145/3460210.3493540},\\n\\tdoi = {10.1145/3460210.3493540},\\n\\tlanguage = {en},\\n\\turldate = {2021-12-08},\\n\\tbooktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},\\n\\tpublisher = {ACM},\\n\\tauthor = {Pernisch, Romana and Dell'Aglio, Daniele and Bernstein, Abraham},\\n\\tmonth = dec,\\n\\tyear = {2021},\\n\\tpages = {177--184},\\n}\\n\",\"author_short\":[\"Pernisch, R.\",\"Dell'Aglio, D.\",\"Bernstein, A.\"],\"key\":\"pernisch_toward_2021\",\"id\":\"pernisch_toward_2021\",\"bibbaseid\":\"pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://dl.acm.org/doi/10.1145/3460210.3493540\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; 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	title = {How {Does} {Knowledge} {Evolve} in {Open} {Knowledge} {Graphs}?},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	url = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},
	doi = {10.4230/TGDK.1.1.11},
	abstract = {Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.},
	language = {en},
	urldate = {2023-12-20},
	journal = {DROPS-IDN/v2/document/10.4230/TGDK.1.1.11},
	author = {Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell'Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jiménez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},
	year = {2023},
}



@inproceedings{verkijk_you_2023,
	address = {New York, NY, USA},
	series = {K-{CAP} '23},
	title = {Do you catch my drift? {On} the usage of embedding methods to measure concept shift in knowledge graphs},
	isbn = {9798400701412},
	shorttitle = {Do you catch my drift?},
	url = {https://dl.acm.org/doi/10.1145/3587259.3627555},
	doi = {10.1145/3587259.3627555},
	abstract = {Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.},
	urldate = {2023-12-20},
	booktitle = {Proceedings of the 12th {Knowledge} {Capture} {Conference} 2023},
	publisher = {Association for Computing Machinery},
	author = {Verkijk, Stella and Roothaert, Ritten and Pernisch, Romana and Schlobach, Stefan},
	month = dec,
	year = {2023},
	keywords = {Concept Shift, Knowledge Graph Embeddings, NLP, Semantics},
	pages = {70--74},
}



@incollection{cochez_approximate_2023,
	series = {Frontiers in {Artificial} {Intelligence} and {Applications}},
	title = {Approximate {Answering} of {Graph} {Queries}},
	volume = {369},
	isbn = {978-1-64368-407-9},
	url = {https://doi.org/10.3233/FAIA230149},
	booktitle = {Compendium of {Neurosymbolic} {Artificial} {Intelligence}},
	publisher = {IOS Press},
	author = {Cochez, Michael and Alivanistos, Dimitrios and Arakelyan, Erik and Berrendorf, Max and Daza, Daniel and Galkin, Mikhail and Minervini, Pasquale and Niepert, Mathias and Ren, Hongyu},
	editor = {Hitzler, Pascal and Sarker, Md Kamruzzaman and Eberhart, Aaron},
	year = {2023},
	doi = {10.3233/FAIA230149},
	pages = {373--386},
}



@article{renaux_knowledge_2023,
	title = {A knowledge graph approach to predict and interpret disease-causing gene interactions},
	volume = {24},
	url = {https://doi.org/10.1186/s12859-023-05451-5},
	doi = {10.1186/S12859-023-05451-5},
	number = {1},
	journal = {BMC Bioinformatics},
	author = {Renaux, Alexandre and Terwagne, Chloé and Cochez, Michael and Tiddi, Ilaria and Nowé, Ann and Lenaerts, Tom},
	year = {2023},
	pages = {324},
}



@article{karim_explainable_2023,
	title = {Explainable {AI} for {Bioinformatics}: {Methods}, {Tools} and {Applications}},
	volume = {24},
	url = {https://doi.org/10.1093/bib/bbad236},
	doi = {10.1093/BIB/BBAD236},
	number = {5},
	journal = {Briefings Bioinformatics},
	author = {Karim, Md Rezaul and Islam, Tanhim and Shajalal, Md and Beyan, Oya and Lange, Christoph and Cochez, Michael and Rebholz-Schuhmann, Dietrich and Decker, Stefan},
	month = oct,
	year = {2023},
}



@inproceedings{ducu_qualifier_2023,
	address = {Athens, Greece},
	title = {Qualifier {Recommendation} for {Wikidata}},
	url = {https://wikidataworkshop.github.io/2023/#mu-sessions},
	abstract = {Wikidata, a collaborative knowledge base for structured data, empowers both human and machine users to contribute and access information. Its main role is in supporting Wikimedia projects by acting as the central storage database for the Wikimedia movement. To optimize the manual process of adding new facts, Wikidata utilizes the association rule-based PropertySuggester tool. However, a recent paper introduced the SchemaTree, a novel approach that surpasses the state-of-the-art PropertySuggester in all performance metrics. The new recommender employs a trie-based method and frequentist inference to efficiently learn and represent property set probabilities within RDF graphs. In this paper, we adapt that recommendation approach, to recommend qualifiers. Specifically, we want to find out whether the recommendation can be done using co-occurrence information of the qualifiers, or whether type information of the item and the value of statements improves performance. We found that the qualifier recommender that uses co-occurring qualifiers and type information leads to the best performance.},
	author = {Ducu, Andrei Mihai and Cochez, Michael},
	month = nov,
	year = {2023},
}



@article{biswas_knowledge_2023,
	title = {Knowledge {Graph} {Embeddings}: {Open} {Challenges} and {Opportunities}},
	volume = {1},
	number = {1},
	journal = {Transactions on Graph Data and Knowledge},
	author = {Biswas, Russa and Kaffee, Lucie-Aimée and Cochez, Michael and Dumbrava, Stefania and Jendal, Theis and Lissandrini, Matteo and Lopez, Vanessa and Menc, Eneldo Loza and Paulheim, Heiko and Sack, Harald and Vakaj, Edlira Kalemi and de Melo, Gerard},
	month = dec,
	year = {2023},
}



@inproceedings{arakelyan_adapting_2023,
	address = {New Orleans},
	title = {Adapting {Neural} {Link} {Predictors} for {Data}-{Efficient} {Complex} {Query} {Answering}},
	url = {https://openreview.net/forum?id=1G7CBp8o7L},
	doi = {10.48550/arXiv.2301.12313},
	abstract = {Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD
, a parameter-efficient score {\textbackslash}emph\{adaptation\} model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component -- which only increases the number of model parameters by 
 -- is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD
 produces significantly more accurate results than current state-of-the-art methods, improving from 
 to 
 Mean Reciprocal Rank values averaged across all datasets and query types while using 
 of the available training query types. We further show that CQD
 is data-efficient, achieving competitive results with only 
 of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.},
	publisher = {OpenReview.net},
	author = {Arakelyan, Erik and Minervini, Pasquale and Daza, Daniel and Cochez, Michael and Augenstein, Isabelle},
	month = jul,
	year = {2023},
	note = {arXiv:2301.12313 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
}



@inproceedings{xiong_reasoning_2023,
	title = {Reasoning beyond {Triples}: {Recent} {Advances} in {Knowledge} {Graph} {Embeddings}},
	url = {https://doi.org/10.1145/3583780.3615294},
	doi = {10.1145/3583780.3615294},
	booktitle = {Proceedings of the 32nd {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}, {CIKM} 2023, {Birmingham}, {United} {Kingdom}, {October} 21-25, 2023},
	publisher = {ACM},
	author = {Xiong, Bo and Nayyeri, Mojtaba and Daza, Daniel and Cochez, Michael},
	editor = {Frommholz, Ingo and Hopfgartner, Frank and Lee, Mark and Oakes, Michael and Lalmas, Mounia and Zhang, Min and Santos, Rodrygo L. T.},
	year = {2023},
	pages = {5228--5231},
}



@article{scherp_structural_2023,
	title = {Structural {Summarization} of {Semantic} {Graphs} {Using} {Quotients}},
	volume = {1},
	abstract = {Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. 
We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. 

In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features.
Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.},
	number = {1},
	journal = {Transactions on Graph Data and Knowledge},
	author = {Scherp, Ansgar and Richerby, David and Blume, Till and Cochez, Michael and Rau, Jannik},
	month = dec,
	year = {2023},
}



@article{daza_bioblp_2023,
	title = {{BioBLP}: a modular framework for learning on multimodal biomedical knowledge graphs},
	volume = {14},
	issn = {2041-1480},
	url = {https://doi.org/10.1186/s13326-023-00301-y},
	doi = {10.1186/s13326-023-00301-y},
	abstract = {Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain.},
	number = {1},
	journal = {Journal of Biomedical Semantics},
	author = {Daza, Daniel and Alivanistos, Dimitrios and Mitra, Payal and Pijnenburg, Thom and Cochez, Michael and Groth, Paul},
	month = dec,
	year = {2023},
	pages = {20},
}



@inproceedings{pesquita_descriptive_2023,
	address = {Cham},
	title = {Descriptive {Comparison} of {Visual} {Ontology} {Change} {Summarisation} {Methods}},
	volume = {13998},
	isbn = {9783031434570 9783031434587},
	url = {https://link.springer.com/10.1007/978-3-031-43458-7_10},
	language = {en},
	urldate = {2023-11-13},
	booktitle = {The {Semantic} {Web}: {ESWC} 2023 {Satellite} {Events}},
	publisher = {Springer Nature Switzerland},
	author = {Chung, Kornpol and Pernisch, Romana and Schlobach, Stefan},
	editor = {Pesquita, Catia and Skaf-Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, Cassia and Hertling, Sven},
	year = {2023},
	doi = {10.1007/978-3-031-43458-7_10},
	pages = {54--58},
}



@inproceedings{prodan_towards_2022,
	title = {Towards {Extreme} and {Sustainable} {Graph} {Processing} for {Urgent} {Societal} {Challenges} in {Europe}},
	doi = {10.1109/CloudSummit54781.2022.00010},
	abstract = {The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70\% more efficient analytics than AliGraph, and 30 \% improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 \% lower greenhouse gas emissions for basic graph operations.},
	booktitle = {2022 {IEEE} {Cloud} {Summit}},
	author = {Prodan, Radu and Kimovski, Dragi and Bartolini, Andrea and Cochez, Michael and Iosup, Alexandru and Kharlamov, Evgeny and Rožanec, Jože and Vasiliu, Laurenţiu and Vărbănescu, Ana Lucia},
	month = oct,
	year = {2022},
	keywords = {Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability},
	pages = {23--30},
}



@inproceedings{vardasbi_intersection_2022,
	address = {New York, NY, USA},
	series = {{CIKM} '22},
	title = {Intersection of {Parallels} as an {Early} {Stopping} {Criterion}},
	isbn = {978-1-4503-9236-5},
	url = {https://dl.acm.org/doi/10.1145/3511808.3557366},
	doi = {10.1145/3511808.3557366},
	abstract = {A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in information retrieval. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.},
	urldate = {2023-04-21},
	booktitle = {Proceedings of the 31st {ACM} {International} {Conference} on {Information} \& {Knowledge} {Management}},
	publisher = {Association for Computing Machinery},
	author = {Vardasbi, Ali and de Rijke, Maarten and Dehghani, Mostafa},
	month = oct,
	year = {2022},
	keywords = {cosine distance, early stopping, generalization, overparameterization},
	pages = {1965--1974},
}



@inproceedings{vardasbi_probabilistic_2022,
	address = {New York, NY, USA},
	series = {{SIGIR} '22},
	title = {Probabilistic {Permutation} {Graph} {Search}: {Black}-{Box} {Optimization} for {Fairness} in {Ranking}},
	isbn = {978-1-4503-8732-3},
	shorttitle = {Probabilistic {Permutation} {Graph} {Search}},
	url = {https://doi.org/10.1145/3477495.3532045},
	doi = {10.1145/3477495.3532045},
	abstract = {There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. {\textbackslash}acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, {\textbackslash}acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to{\textasciitilde}{\textbackslash}acPL, our distribution representation, called{\textasciitilde}{\textbackslash}acPPG, can be used for black-box optimization of fairness. Different from{\textasciitilde}{\textbackslash}acPL, where pointwise logits are used as the distribution parameters, in{\textasciitilde}{\textbackslash}acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making{\textasciitilde}{\textbackslash}acPPG suitable for both deterministic and stochastic rankings. Our experiments show that{\textasciitilde}{\textbackslash}acPPG, while comparable to{\textasciitilde}{\textbackslash}acPL for larger session repetitions (i.e., stochastic ranking), improves over{\textasciitilde}{\textbackslash}acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of {\textbackslash}acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as "item \$d\_1\$ should always be ranked higher than item \$d\_2\$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.},
	urldate = {2023-04-21},
	booktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {Association for Computing Machinery},
	author = {Vardasbi, Ali and Sarvi, Fatemeh and de Rijke, Maarten},
	month = jul,
	year = {2022},
	keywords = {fairness in ranking, permutation distribution, permutation graph, plackett-luce},
	pages = {715--725},
}



@article{wang_recommending_2022,
	title = {Recommending scientific datasets using author networks in ensemble methods},
	volume = {5},
	issn = {2451-8484},
	url = {https://content.iospress.com/articles/data-science/ds220056},
	doi = {10.3233/DS-220056},
	abstract = {Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation : the task of re},
	language = {en},
	number = {2},
	urldate = {2023-04-22},
	journal = {Data Science},
	author = {Wang, Xu and van Harmelen, Frank and Huang, Zhisheng},
	month = jul,
	year = {2022},
	note = {Publisher: IOS Press},
	pages = {167--193},
}



@inproceedings{chakrabarti_unsupervised_2021,
	address = {Cham},
	title = {Unsupervised {Feature} {Selection} for {Efficient} {Exploration} of {High} {Dimensional} {Data}},
	isbn = {978-3-030-82472-3},
	url = {https://www.cochez.nl/papers/feature_selection_for_exploration.pdf},
	abstract = {The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.},
	booktitle = {Advances in {Databases} and {Information} {Systems}},
	publisher = {Springer International Publishing},
	author = {Chakrabarti, Arnab and Das, Abhijeet and Cochez, Michael and Quix, Christoph},
	editor = {Bellatreche, Ladjel and Dumas, Marlon and Karras, Panagiotis and Matulevičius, Raimundas},
	month = aug,
	year = {2021},
	pages = {183--197},
}



@article{karim_deepkneeexplainer_2021,
	title = {{DeepKneeExplainer}: {Explainable} {Knee} {Osteoarthritis} {Diagnosis} {From} {Radiographs} and {Magnetic} {Resonance} {Imaging}},
	volume = {9},
	doi = {10.1109/ACCESS.2021.3062493},
	journal = {IEEE Access},
	author = {Karim, Md. Rezaul and Jiao, Jiao and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},
	month = feb,
	year = {2021},
	pages = {39757--39780},
}



@inproceedings{van_bakel_approximate_2021,
	address = {Cham},
	title = {Approximate {Knowledge} {Graph} {Query} {Answering}: {From} {Ranking} to {Binary} {Classification}},
	isbn = {978-3-030-72308-8},
	abstract = {Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.},
	booktitle = {Graph {Structures} for {Knowledge} {Representation} and {Reasoning}},
	publisher = {Springer International Publishing},
	author = {van Bakel, Ruud and Aleksiev, Teodor and Daza, Daniel and Alivanistos, Dimitrios and Cochez, Michael},
	editor = {Cochez, Michael and Croitoru, Madalina and Marquis, Pierre and Rudolph, Sebastian},
	month = sep,
	year = {2021},
	pages = {107--124},
}



@inproceedings{lipp_towards_2021,
	address = {Cham},
	title = {Towards {Easy} {Vocabulary} {Drafts} with {Neologism} 2.0},
	isbn = {978-3-030-80418-3},
	url = {https://openreview.net/forum?id=PoAI5RrlUFj},
	abstract = {Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.},
	booktitle = {The {Semantic} {Web}: {ESWC} 2021 {Satellite} {Events}},
	publisher = {Springer International Publishing},
	author = {Lipp, Johannes and Gleim, Lars and Cochez, Michael and Dimitriadis, Iraklis and Ali, Hussain and Alvarez, Daniel Hoppe and Lange, Christoph and Decker, Stefan},
	editor = {Verborgh, Ruben and Dimou, Anastasia and Hogan, Aidan and d'Amato, Claudia and Tiddi, Ilaria and Bröring, Arne and Mayer, Simon and Ongenae, Femke and Tommasini, Riccardo and Alam, Mehwish},
	month = jun,
	year = {2021},
	keywords = {discoverylab},
	pages = {21--26},
}



@inproceedings{xiong_hyperbolic_2022,
	title = {Hyperbolic {Embedding} {Inference} for {Structured} {Multi}-{Label} {Prediction}},
	booktitle = {{NeurIPS2022}},
	author = {Xiong, Bo and Cochez, Michael and Nayyeri, Mojtaba and Staab, Steffen},
	month = nov,
	year = {2022},
}



@inproceedings{harper_question_2022,
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Question {Answering} with {Additive} {Restrictive} {Training} ({QuAART}): {Question} {Answering} for the {Rapid} {Development} of {New} {Knowledge} {Extraction} {Pipelines}},
	volume = {13514},
	url = {https://doi.org/10.1007/978-3-031-17105-5\_4},
	doi = {10.1007/978-3-031-17105-5_4},
	booktitle = {Knowledge {Engineering} and {Knowledge} {Management} - 23rd {International} {Conference}, {EKAW} 2022, {Bolzano}, {Italy}, {September} 26-29, 2022, {Proceedings}},
	publisher = {Springer},
	author = {Harper, Corey A. and Jr, Ron Daniel and Groth, Paul},
	editor = {Corcho, Óscar and Hollink, Laura and Kutz, Oliver and Troquard, Nicolas and Ekaputra, Fajar J.},
	month = sep,
	year = {2022},
	pages = {51--65},
}



@inproceedings{wang_scientific_2022,
	address = {Cham},
	title = {Scientific {Item} {Recommendation} {Using} a {Citation} {Network}},
	isbn = {978-3-031-10986-7},
	booktitle = {Knowledge {Science}, {Engineering} and {Management}},
	publisher = {Springer International Publishing},
	author = {Wang, Xu and van Harmelen, Frank and Cochez, Michael and Huang, Zhisheng", editor="Memmi, Gerard and Yang, Baijian and Kong, Linghe and Zhang, Tianwei and Qiu, Meikang},
	month = aug,
	year = {2022},
	pages = {469--484},
}



@article{barthelemy_towards_2022,
	title = {Towards a standard-based open data ecosystem: analysis of {DCAT}-{AP} use at national and {European} level},
	volume = {18},
	number = {2},
	journal = {Electronic Government, an International Journal},
	author = {Barthelemy, Florian and Cochez, Michael and Dimitriadis, Iraklis and Karim, Naila and Loutas, Nikolaos and Magnisalis, Ioannis and Comet, Lina Molinas and Peristeras, Vassilios and Wyns, Brecht},
	month = jan,
	year = {2022},
	note = {Publisher: Inderscience Publishers (IEL)},
	pages = {137--180},
}



@inproceedings{generale_scaling_2022,
	address = {Virtual Event, Lyon, France},
	series = {{WWW} '22},
	title = {Scaling {R}-{GCN} {Training} with {Graph} {Summarization}},
	isbn = {978-1-4503-9130-6},
	url = {https://arxiv.org/abs/2203.02622},
	doi = {https://doi.org/10.1145/3487553.3524719},
	booktitle = {{WWW} '22: {Companion} {Proceedings} of the {Web} {Conference} 2022},
	publisher = {Association for Computing Machinery},
	author = {Generale, Alessandro and Blume, Till and Cochez, Michael},
	month = apr,
	year = {2022},
	note = {arXiv preprint arXiv:2203.02622},
	keywords = {graph neural network, graph summarization, scalability},
	pages = {1073--1082},
}



@inproceedings{mansoury_exposure-aware_2022,
	title = {Exposure-{Aware} {Recommendation} using {Contextual} {Bandits}},
	copyright = {Creative Commons Attribution 4.0 International},
	url = {https://arxiv.org/abs/2209.01665},
	doi = {10.48550/ARXIV.2209.01665},
	publisher = {5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022},
	author = {Mansoury, Masoud and Mobasher, Bamshad and van Hoof, Herke},
	month = sep,
	year = {2022},
	keywords = {FOS: Computer and information sciences, Information Retrieval (cs.IR)},
}



@inproceedings{alivanistos_query_2022,
	title = {Query {Embedding} on {Hyper}-{Relational} {Knowledge} {Graphs}},
	url = {https://openreview.net/forum?id=4rLw09TgRw9},
	booktitle = {The {Tenth} {International} {Conference} on {Learning} {Representations}, {ICLR} 2022, {Virtual} {Event}, {April} 25-29, 2022},
	publisher = {OpenReview.net},
	author = {Alivanistos, Dimitrios and Berrendorf, Max and Cochez, Michael and Galkin, Mikhail},
	month = apr,
	year = {2022},
}



@inproceedings{colombo_potential_2022,
	series = {{CEUR} {Workshop} {Proceedings}},
	title = {Potential {Energy} to {Improve} {Link} {Prediction} {With} {Relational} {Graph} {Neural} {Networks}},
	volume = {3121},
	url = {http://ceur-ws.org/Vol-3121/short2.pdf},
	booktitle = {Proceedings of the {AAAI} 2022 {Spring} {Symposium} on {Machine} {Learning} and {Knowledge} {Engineering} for {Hybrid} {Intelligence} ({AAAI}-{MAKE} 2022), {Stanford} {University}, {Palo} {Alto}, {California}, {USA}, {March} 21-23, 2022},
	publisher = {CEUR-WS.org},
	author = {Colombo, Simone and Alivanistos, Dimitrios and Cochez, Michael},
	editor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and Harmelen, Frank van},
	month = mar,
	year = {2022},
}



@inproceedings{daza_inductive_2021,
	title = {Inductive {Entity} {Representations} from {Text} via {Link} {Prediction}},
	url = {https://doi.org/10.1145/3442381.3450141},
	doi = {10.1145/3442381.3450141},
	booktitle = {{WWW} '21: {The} {Web} {Conference} 2021, {Virtual} {Event} / {Ljubljana}, {Slovenia}, {April} 19-23, 2021},
	publisher = {ACM / IW3C2},
	author = {Daza, Daniel and Cochez, Michael and Groth, Paul},
	editor = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila},
	month = apr,
	year = {2021},
	pages = {798--808},
}



@inproceedings{arakelyan_complex_2021,
	title = {Complex {Query} {Answering} with {Neural} {Link} {Predictors}},
	url = {https://openreview.net/forum?id=Mos9F9kDwkz},
	booktitle = {International {Conference} on {Learning} {Representations} ({ICLR} 2021)},
	publisher = {Openreview},
	author = {Arakelyan, Erik and Daza, Daniel and Minervini, Pasquale and Cochez, Michael},
	month = may,
	year = {2021},
	note = {\_eprint: 2011.03459},
}



@inproceedings{merono-penuela_multi-domain_2021,
	address = {New York, NY, USA},
	series = {K-{CAP} '21},
	title = {Multi-{Domain} and {Explainable} {Prediction} of {Changes} in {Web} {Vocabularies}},
	isbn = {978-1-4503-8457-5},
	url = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583},
	doi = {10.1145/3460210.3493583},
	abstract = {Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.},
	language = {English},
	booktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},
	publisher = {Association for Computing Machinery},
	author = {Meroño-Peñuela, Albert and Pernisch, Romana and Guéret, Christophe and Schlobach, Stefan},
	month = dec,
	year = {2021},
	note = {event-place: Virtual Event, USA},
	keywords = {change modelling, ontology evolution, vocabulary change},
	pages = {193--200},
}



@inproceedings{kim_machine_2023,
	title = {A {Machine} {With} {Human}-{Like} {Memory} {Systems}},
	booktitle = {{arXiv} preprint {arXiv}:2204.01611},
	author = {Kim, Taewoon and Cochez, Michael and Francois-Lavet, Vincent and Neerincx, Mark and Vossen, Piek},
	year = {2023},
}



@inproceedings{alivanistos_prompting_2022,
	title = {Prompting as {Probing}: {Using} {Language} {Models} for {Knowledge} {Base} {Construction}},
	volume = {3274},
	copyright = {arXiv.org perpetual, non-exclusive license},
	url = {https://ceur-ws.org/Vol-3274/paper2.pdf},
	doi = {10.48550/ARXIV.2208.11057},
	booktitle = {Proceedings of the {Semantic} {Web} {Challenge} on {Knowledge} {Base} {Construction} from {Pre}-trained {Language} {Models} 2022 co-located with the 21st {International} {Semantic} {Web} {Conference} ({ISWC2022})},
	publisher = {CEUR-ws.org},
	author = {Alivanistos, Dimitrios and Santamaría, Selene Báez and Cochez, Michael and Kalo, Jan-Christoph and van Krieken, Emile and Thanapalasingam, Thiviyan},
	month = oct,
	year = {2022},
	keywords = {Artificial Intelligence (cs.AI), Computation and Language (cs.CL), FOS: Computer and information sciences},
	pages = {11--34},
}



@inproceedings{pal_parameter-efficient_2023,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Parameter-{Efficient} {Sparse} {Retrievers} and {Rerankers} {Using} {Adapters}},
	isbn = {978-3-031-28238-6},
	doi = {10.1007/978-3-031-28238-6_2},
	abstract = {Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2\% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade.)},
	language = {en},
	booktitle = {Advances in {Information} {Retrieval}},
	publisher = {Springer Nature Switzerland},
	author = {Pal, Vaishali and Lassance, Carlos and Déjean, Hervé and Clinchant, Stéphane},
	editor = {Kamps, Jaap and Goeuriot, Lorraine and Crestani, Fabio and Maistro, Maria and Joho, Hideo and Davis, Brian and Gurrin, Cathal and Kruschwitz, Udo and Caputo, Annalina},
	year = {2023},
	keywords = {Adapters, Information Retrieval, Sparse neural retriever},
	pages = {16--31},
}



@inproceedings{penha_seventh_2022,
	address = {New York, NY, USA},
	series = {{SIGIR} '22},
	title = {The {Seventh} {Workshop} on {Search}-{Oriented} {Conversational} {Artificial} {Intelligence} ({SCAI}'22)},
	isbn = {978-1-4503-8732-3},
	url = {https://dl.acm.org/doi/10.1145/3477495.3531700},
	doi = {10.1145/3477495.3531700},
	abstract = {The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.},
	urldate = {2023-04-20},
	booktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},
	publisher = {Association for Computing Machinery},
	author = {Penha, Gustavo and Vakulenko, Svitlana and Dusek, Ondrej and Clark, Leigh and Pal, Vaishali and Adlakha, Vaibhav},
	month = jul,
	year = {2022},
	keywords = {conversational information access, conversational search, information-seeking dialogue},
	pages = {3466--3469},
}



@inproceedings{abdollahpouri_mors_2021,
	address = {Amsterdam Netherlands},
	title = {{MORS} 2021: 1st {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},
	isbn = {978-1-4503-8458-2},
	shorttitle = {{MORS} 2021},
	url = {https://dl.acm.org/doi/10.1145/3460231.3470936},
	doi = {10.1145/3460231.3470936},
	language = {en},
	urldate = {2023-04-20},
	booktitle = {Fifteenth {ACM} {Conference} on {Recommender} {Systems}},
	publisher = {ACM},
	author = {Abdollahpouri, Himan and Elahi, Mehdi and Mansoury, Masoud and Sahebi, Shaghayegh and Nazari, Zahra and Chaney, Allison and Loni, Babak},
	month = sep,
	year = {2021},
	pages = {787--788},
}



@article{hogan_knowledge_2021,
	title = {Knowledge {Graphs}},
	volume = {54},
	issn = {0360-0300},
	url = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772},
	doi = {10.1145/3447772},
	abstract = {In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.},
	number = {4},
	journal = {ACM Comput. Surv.},
	author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and D’amato, Claudia and Melo, Gerard De and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and Ngomo, Axel-Cyrille Ngonga and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},
	month = jul,
	year = {2021},
	note = {Place: New York, NY, USA
Publisher: Association for Computing Machinery},
	keywords = {Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes},
}



@inproceedings{abdollahpouri_mors_2022,
	address = {Seattle WA USA},
	title = {{MORS} 2022: {The} {Second} {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},
	isbn = {978-1-4503-9278-5},
	shorttitle = {{MORS} 2022},
	url = {https://dl.acm.org/doi/10.1145/3523227.3547410},
	doi = {10.1145/3523227.3547410},
	language = {en},
	urldate = {2023-04-20},
	booktitle = {Sixteenth {ACM} {Conference} on {Recommender} {Systems}},
	publisher = {ACM},
	author = {Abdollahpouri, Himan and Sahebi, Shaghayegh and Elahi, Mehdi and Mansoury, Masoud and Loni, Babak and Nazari, Zahra and Dimakopoulou, Maria},
	month = sep,
	year = {2022},
	pages = {658--660},
}



@inproceedings{daza_slotgan_2022,
	address = {Dublin, Ireland},
	title = {{SlotGAN}: {Detecting} {Mentions} in {Text} via {Adversarial} {Distant} {Learning}},
	url = {https://aclanthology.org/2022.spnlp-1.4},
	doi = {10.18653/v1/2022.spnlp-1.4},
	abstract = {We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95\% of the precision of a supervised method, and 84\% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.},
	booktitle = {Proceedings of the {Sixth} {Workshop} on {Structured} {Prediction} for {NLP}},
	publisher = {Association for Computational Linguistics},
	author = {Daza, Daniel and Cochez, Michael and Groth, Paul},
	month = may,
	year = {2022},
	pages = {32--39},
}



@inproceedings{karim_deepcovidexplainer_2020,
	title = {{DeepCOVIDExplainer}: {Explainable} {COVID}-19 {Diagnosis} from {Chest} {X}-ray {Images}},
	url = {https://arxiv.org/abs/2004.04582},
	doi = {10.1109/BIBM49941.2020.9313304},
	booktitle = {2020 {IEEE} {International} {Conference} on {Bioinformatics} and {Biomedicine} ({BIBM})},
	author = {Karim, Md. Rezaul and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},
	year = {2020},
	pages = {1034--1037},
}



@inproceedings{karim_classification_2020,
	title = {Classification {Benchmarks} for {Under}-resourced {Bengali} {Language} based on {Multichannel} {Convolutifonal}-{LSTM} {Network}},
	url = {https://arxiv.org/abs/2004.07807},
	doi = {10.1109/DSAA49011.2020.00053},
	booktitle = {2020 {IEEE} 7th {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})},
	author = {Karim, Md. Rezaul and Raja Chakravarthi, Bharathi and McCrae, John P. and Cochez, Michael},
	year = {2020},
	pages = {390--399},
}



@inproceedings{pellegrino_geval_2020,
	address = {Cham},
	title = {{GEval}: {A} {Modular} and {Extensible} {Evaluation} {Framework} for {Graph} {Embedding} {Techniques}},
	isbn = {978-3-030-49461-2},
	abstract = {While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.},
	booktitle = {The {Semantic} {Web}},
	publisher = {Springer International Publishing},
	author = {Pellegrino, Maria Angela and Altabba, Abdulrahman and Garofalo, Martina and Ristoski, Petar and Cochez, Michael},
	editor = {Harth, Andreas and Kirrane, Sabrina and Ngonga Ngomo, Axel-Cyrille and Paulheim, Heiko and Rula, Anisa and Gentile, Anna Lisa and Haase, Peter and Cochez, Michael},
	year = {2020},
	pages = {565--582},
}



@article{karim_deep_2020,
	title = {Deep learning-based clustering approaches for bioinformatics},
	issn = {1477-4054},
	url = {https://doi.org/10.1093/bib/bbz170},
	doi = {10.1093/bib/bbz170},
	abstract = {Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.},
	journal = {Briefings in Bioinformatics},
	author = {Karim, Md Rezaul and Beyan, Oya and Zappa, Achille and Costa, Ivan G and Rebholz-Schuhmann, Dietrich and Cochez, Michael and Decker, Stefan},
	month = feb,
	year = {2020},
	note = {\_eprint: https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbz170/32313172/bbz170.pdf},
}



@article{wang_structured_2020,
	title = {Structured query construction via knowledge graph embedding},
	volume = {62},
	issn = {0219-3116},
	url = {https://doi.org/10.1007/s10115-019-01401-x},
	doi = {10.1007/s10115-019-01401-x},
	abstract = {In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.},
	number = {5},
	journal = {Knowledge and Information Systems},
	author = {Wang, Ruijie and Wang, Meng and Liu, Jun and Cochez, Michael and Decker, Stefan},
	month = may,
	year = {2020},
	pages = {1819--1846},
}



@book{hogan_knowledge_2021-1,
	series = {Synthesis {Lectures} on {Data}, {Semantics}, and {Knowledge}},
	title = {Knowledge graphs},
	volume = {12},
	isbn = {978-3-031-01918-0},
	url = {https://kgbook.org},
	publisher = {Springer (formerly Morgan \& Claypool Publishers)},
	author = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d'Amato, Claudia and Melo, Gerard de and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and {others}},
	year = {2021},
	doi = {10.2200/S01125ED1V01Y202109DSK022},
}



@inproceedings{daza_message_2020,
	title = {Message {Passing} {Query} {Embedding}},
	url = {https://arxiv.org/abs/2002.02406},
	booktitle = {{ICML} {Workshop} - {Graph} {Representation} {Learning} and {Beyond}},
	author = {Daza, Daniel and Cochez, Michael},
	year = {2020},
}



@article{pernisch_visualising_2022,
	title = {Visualising the effects of ontology changes and studying their understanding with {ChImp}},
	volume = {74},
	issn = {1570-8268},
	url = {https://www.sciencedirect.com/science/article/pii/S1570826822000117},
	doi = {10.1016/j.websem.2022.100715},
	abstract = {Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.},
	language = {en},
	urldate = {2022-06-20},
	journal = {Journal of Web Semantics},
	author = {Pernisch, Romana and Dell’Aglio, Daniele and Serbak, Mirko and Gonçalves, Rafael S. and Bernstein, Abraham},
	month = oct,
	year = {2022},
	keywords = {Materialisation, Ontology editing, Ontology evolution impact, User study},
	pages = {100715},
}



@inproceedings{pernisch_toward_2021,
	address = {Virtual Event USA},
	title = {Toward {Measuring} the {Resemblance} of {Embedding} {Models} for {Evolving} {Ontologies}},
	isbn = {978-1-4503-8457-5},
	url = {https://dl.acm.org/doi/10.1145/3460210.3493540},
	doi = {10.1145/3460210.3493540},
	language = {en},
	urldate = {2021-12-08},
	booktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},
	publisher = {ACM},
	author = {Pernisch, Romana and Dell'Aglio, Daniele and Bernstein, Abraham},
	month = dec,
	year = {2021},
	pages = {177--184},
}
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In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (14)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023', 'https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11', event);\">\n    How Does Knowledge Evolve in Open Knowledge Graphs?.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Polleres, A.; Pernisch, R.; Bonifati, A.; Dell'Aglio, D.; Dobriy, D.; Dumbrava, S.; Etcheverry, L.; Ferranti, N.; Hose, K.; Jiménez-Ruiz, E.; Lissandrini, M.; Scherp, A.; Tommasini, R.;  and Wachs, J.\n</span>\n\n  \n\n</span>\n\n  <i>DROPS-IDN/v2/document/10.4230/TGDK.1.1.11</i>.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11\"\n     onclick=\"log_download('polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023', 'https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"How Does Knowledge Evolve in Open Knowledge Graphs? [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.4230/TGDK.1.1.11\"\n     onclick=\"log_download('polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023', 'http://doi.org/10.4230/TGDK.1.1.11', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('polleres-pernisch-bonifati-dellaglio-dobriy-dumbrava-etcheverry-ferranti-etal-howdoesknowledgeevolveinopenknowledgegraphs-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{polleres_how_2023,\n\ttitle = {How {Does} {Knowledge} {Evolve} in {Open} {Knowledge} {Graphs}?},\n\tcopyright = {https://creativecommons.org/licenses/by/4.0/legalcode},\n\turl = {https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.11},\n\tdoi = {10.4230/TGDK.1.1.11},\n\tabstract = {Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.},\n\tlanguage = {en},\n\turldate = {2023-12-20},\n\tjournal = {DROPS-IDN/v2/document/10.4230/TGDK.1.1.11},\n\tauthor = {Polleres, Axel and Pernisch, Romana and Bonifati, Angela and Dell&#x27;Aglio, Daniele and Dobriy, Daniil and Dumbrava, Stefania and Etcheverry, Lorena and Ferranti, Nicolas and Hose, Katja and Jiménez-Ruiz, Ernesto and Lissandrini, Matteo and Scherp, Ansgar and Tommasini, Riccardo and Wachs, Johannes},\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the collective management of evolving knowledge. The present work aims t o provide an analysis of the obstacles related to investigating and processing specifically this central aspect of evolution in KGs. To this end, we discuss (i) the dimensions of evolution in KGs, (ii) the observability of evolution in existing, open, collaboratively constructed Knowledge Graphs over time, and (iii) possible metrics to analyse this evolution. We provide an overview of relevant state-of-the-art research, ranging from metrics developed for Knowledge Graphs specifically to potential methods from related fields such as network science. Additionally, we discuss technical approaches - and their current limitations - related to storing, analysing and processing large and evolving KGs in terms of handling typical KG downstream tasks.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3587259.3627555\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023', 'https://dl.acm.org/doi/10.1145/3587259.3627555', event);\">\n    Do you catch my drift? On the usage of embedding methods to measure concept shift in knowledge graphs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Verkijk, S.; Roothaert, R.; Pernisch, R.;  and Schlobach, S.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 12th Knowledge Capture Conference 2023</i>, of <i>K-CAP '23</i>, pages 70–74, New York, NY, USA, December 2023. Association for Computing Machinery\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3587259.3627555\"\n     onclick=\"log_download('verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023', 'https://dl.acm.org/doi/10.1145/3587259.3627555', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Do you catch my drift? On the usage of embedding methods to measure concept shift in knowledge graphs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3587259.3627555\"\n     onclick=\"log_download('verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023', 'http://doi.org/10.1145/3587259.3627555', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('verkijk-roothaert-pernisch-schlobach-doyoucatchmydriftontheusageofembeddingmethodstomeasureconceptshiftinknowledgegraphs-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{verkijk_you_2023,\n\taddress = {New York, NY, USA},\n\tseries = {K-{CAP} &#x27;23},\n\ttitle = {Do you catch my drift? {On} the usage of embedding methods to measure concept shift in knowledge graphs},\n\tisbn = {9798400701412},\n\tshorttitle = {Do you catch my drift?},\n\turl = {https://dl.acm.org/doi/10.1145/3587259.3627555},\n\tdoi = {10.1145/3587259.3627555},\n\tabstract = {Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.},\n\turldate = {2023-12-20},\n\tbooktitle = {Proceedings of the 12th {Knowledge} {Capture} {Conference} 2023},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Verkijk, Stella and Roothaert, Ritten and Pernisch, Romana and Schlobach, Stefan},\n\tmonth = dec,\n\tyear = {2023},\n\tkeywords = {Concept Shift, Knowledge Graph Embeddings, NLP, Semantics},\n\tpages = {70--74},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatically detecting and measuring differences between evolving Knowledge Graphs (KGs) has been a topic of investigation for years. With the rising popularity of embedding methods, we investigate the possibility of using embeddings to detect Concept Shift in evolving KGs. Specifically, we go deeper into the usage of nearest neighbour set comparison as the basis for a similarity measure, and show why this approach is conceptually problematic. As an alternative, we explore the possibility of using clustering methods. This paper serves to (i) inform the community about the challenges that arise when using KG embeddings for the comparison of different versions of a KG specifically, (ii) investigate how this is supported by theories on knowledge representation and semantic representation in NLP and (iii) take the first steps into the direction of valuable representation of semantics within KGs for comparison.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.3233/FAIA230149\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023', 'https://doi.org/10.3233/FAIA230149', event);\">\n    Approximate Answering of Graph Queries.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cochez, M.; Alivanistos, D.; Arakelyan, E.; Berrendorf, M.; Daza, D.; Galkin, M.; Minervini, P.; Niepert, M.;  and Ren, H.\n</span>\n\n  \n\n</span>\n\n  In Hitzler, P.; Sarker, M. K.;  and Eberhart, A., editor(s), <i>Compendium of Neurosymbolic Artificial Intelligence</i>, volume 369, of Frontiers in Artificial Intelligence and Applications, pages 373–386. IOS Press,  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.3233/FAIA230149\"\n     onclick=\"log_download('cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023', 'https://doi.org/10.3233/FAIA230149', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Approximate Answering of Graph Queries [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3233/FAIA230149\"\n     onclick=\"log_download('cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023', 'http://doi.org/10.3233/FAIA230149', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cochez-alivanistos-arakelyan-berrendorf-daza-galkin-minervini-niepert-etal-approximateansweringofgraphqueries-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{cochez_approximate_2023,\n\tseries = {Frontiers in {Artificial} {Intelligence} and {Applications}},\n\ttitle = {Approximate {Answering} of {Graph} {Queries}},\n\tvolume = {369},\n\tisbn = {978-1-64368-407-9},\n\turl = {https://doi.org/10.3233/FAIA230149},\n\tbooktitle = {Compendium of {Neurosymbolic} {Artificial} {Intelligence}},\n\tpublisher = {IOS Press},\n\tauthor = {Cochez, Michael and Alivanistos, Dimitrios and Arakelyan, Erik and Berrendorf, Max and Daza, Daniel and Galkin, Mikhail and Minervini, Pasquale and Niepert, Mathias and Ren, Hongyu},\n\teditor = {Hitzler, Pascal and Sarker, Md Kamruzzaman and Eberhart, Aaron},\n\tyear = {2023},\n\tdoi = {10.3233/FAIA230149},\n\tpages = {373--386},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1186/s12859-023-05451-5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023', 'https://doi.org/10.1186/s12859-023-05451-5', event);\">\n    A knowledge graph approach to predict and interpret disease-causing gene interactions.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Renaux, A.; Terwagne, C.; Cochez, M.; Tiddi, I.; Nowé, A.;  and Lenaerts, T.\n</span>\n\n  \n\n</span>\n\n  <i>BMC Bioinformatics</i>, 24(1): 324.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1186/s12859-023-05451-5\"\n     onclick=\"log_download('renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023', 'https://doi.org/10.1186/s12859-023-05451-5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A knowledge graph approach to predict and interpret disease-causing gene interactions [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1186/S12859-023-05451-5\"\n     onclick=\"log_download('renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023', 'http://doi.org/10.1186/S12859-023-05451-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('renaux-terwagne-cochez-tiddi-now-lenaerts-aknowledgegraphapproachtopredictandinterpretdiseasecausinggeneinteractions-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{renaux_knowledge_2023,\n\ttitle = {A knowledge graph approach to predict and interpret disease-causing gene interactions},\n\tvolume = {24},\n\turl = {https://doi.org/10.1186/s12859-023-05451-5},\n\tdoi = {10.1186/S12859-023-05451-5},\n\tnumber = {1},\n\tjournal = {BMC Bioinformatics},\n\tauthor = {Renaux, Alexandre and Terwagne, Chloé and Cochez, Michael and Tiddi, Ilaria and Nowé, Ann and Lenaerts, Tom},\n\tyear = {2023},\n\tpages = {324},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1093/bib/bbad236\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023', 'https://doi.org/10.1093/bib/bbad236', event);\">\n    Explainable AI for Bioinformatics: Methods, Tools and Applications.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karim, M. R.; Islam, T.; Shajalal, M.; Beyan, O.; Lange, C.; Cochez, M.; Rebholz-Schuhmann, D.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  <i>Briefings Bioinformatics</i>, 24(5). October 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1093/bib/bbad236\"\n     onclick=\"log_download('karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023', 'https://doi.org/10.1093/bib/bbad236', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Explainable AI for Bioinformatics: Methods, Tools and Applications [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1093/BIB/BBAD236\"\n     onclick=\"log_download('karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023', 'http://doi.org/10.1093/BIB/BBAD236', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karim-islam-shajalal-beyan-lange-cochez-rebholzschuhmann-decker-explainableaiforbioinformaticsmethodstoolsandapplications-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{karim_explainable_2023,\n\ttitle = {Explainable {AI} for {Bioinformatics}: {Methods}, {Tools} and {Applications}},\n\tvolume = {24},\n\turl = {https://doi.org/10.1093/bib/bbad236},\n\tdoi = {10.1093/BIB/BBAD236},\n\tnumber = {5},\n\tjournal = {Briefings Bioinformatics},\n\tauthor = {Karim, Md Rezaul and Islam, Tanhim and Shajalal, Md and Beyan, Oya and Lange, Christoph and Cochez, Michael and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\n\tmonth = oct,\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"ducu-cochez-qualifierrecommendationforwikidata-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://wikidataworkshop.github.io/2023/#mu-sessions\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'ducu-cochez-qualifierrecommendationforwikidata-2023', 'https://wikidataworkshop.github.io/2023/#mu-sessions', event);\">\n    Qualifier Recommendation for Wikidata.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Ducu, A. M.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In Athens, Greece, November 2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://wikidataworkshop.github.io/2023/#mu-sessions\"\n     onclick=\"log_download('ducu-cochez-qualifierrecommendationforwikidata-2023', 'https://wikidataworkshop.github.io/2023/#mu-sessions', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Qualifier Recommendation for Wikidata [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/ducu-cochez-qualifierrecommendationforwikidata-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('ducu-cochez-qualifierrecommendationforwikidata-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{ducu_qualifier_2023,\n\taddress = {Athens, Greece},\n\ttitle = {Qualifier {Recommendation} for {Wikidata}},\n\turl = {https://wikidataworkshop.github.io/2023/#mu-sessions},\n\tabstract = {Wikidata, a collaborative knowledge base for structured data, empowers both human and machine users to contribute and access information. Its main role is in supporting Wikimedia projects by acting as the central storage database for the Wikimedia movement. To optimize the manual process of adding new facts, Wikidata utilizes the association rule-based PropertySuggester tool. However, a recent paper introduced the SchemaTree, a novel approach that surpasses the state-of-the-art PropertySuggester in all performance metrics. The new recommender employs a trie-based method and frequentist inference to efficiently learn and represent property set probabilities within RDF graphs. In this paper, we adapt that recommendation approach, to recommend qualifiers. Specifically, we want to find out whether the recommendation can be done using co-occurrence information of the qualifiers, or whether type information of the item and the value of statements improves performance. We found that the qualifier recommender that uses co-occurring qualifiers and type information leads to the best performance.},\n\tauthor = {Ducu, Andrei Mihai and Cochez, Michael},\n\tmonth = nov,\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Wikidata, a collaborative knowledge base for structured data, empowers both human and machine users to contribute and access information. Its main role is in supporting Wikimedia projects by acting as the central storage database for the Wikimedia movement. To optimize the manual process of adding new facts, Wikidata utilizes the association rule-based PropertySuggester tool. However, a recent paper introduced the SchemaTree, a novel approach that surpasses the state-of-the-art PropertySuggester in all performance metrics. The new recommender employs a trie-based method and frequentist inference to efficiently learn and represent property set probabilities within RDF graphs. In this paper, we adapt that recommendation approach, to recommend qualifiers. Specifically, we want to find out whether the recommendation can be done using co-occurrence information of the qualifiers, or whether type information of the item and the value of statements improves performance. We found that the qualifier recommender that uses co-occurring qualifiers and type information leads to the best performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"biswas-kaffee-cochez-dumbrava-jendal-lissandrini-lopez-menc-etal-knowledgegraphembeddingsopenchallengesandopportunities-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Knowledge Graph Embeddings: Open Challenges and Opportunities.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Biswas, R.; Kaffee, L.; Cochez, M.; Dumbrava, S.; Jendal, T.; Lissandrini, M.; Lopez, V.; Menc, E. L.; Paulheim, H.; Sack, H.; Vakaj, E. K.;  and de Melo, G.\n</span>\n\n  \n\n</span>\n\n  <i>Transactions on Graph Data and Knowledge</i>, 1(1). December 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/biswas-kaffee-cochez-dumbrava-jendal-lissandrini-lopez-menc-etal-knowledgegraphembeddingsopenchallengesandopportunities-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('biswas-kaffee-cochez-dumbrava-jendal-lissandrini-lopez-menc-etal-knowledgegraphembeddingsopenchallengesandopportunities-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{biswas_knowledge_2023,\n\ttitle = {Knowledge {Graph} {Embeddings}: {Open} {Challenges} and {Opportunities}},\n\tvolume = {1},\n\tnumber = {1},\n\tjournal = {Transactions on Graph Data and Knowledge},\n\tauthor = {Biswas, Russa and Kaffee, Lucie-Aimée and Cochez, Michael and Dumbrava, Stefania and Jendal, Theis and Lissandrini, Matteo and Lopez, Vanessa and Menc, Eneldo Loza and Paulheim, Heiko and Sack, Harald and Vakaj, Edlira Kalemi and de Melo, Gerard},\n\tmonth = dec,\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=1G7CBp8o7L\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023', 'https://openreview.net/forum?id=1G7CBp8o7L', event);\">\n    Adapting Neural Link Predictors for Data-Efficient Complex Query Answering.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Arakelyan, E.; Minervini, P.; Daza, D.; Cochez, M.;  and Augenstein, I.\n</span>\n\n  \n\n</span>\n\n  In New Orleans, July 2023. OpenReview.net\n  <span class=\"note\">arXiv:2301.12313 [cs]</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=1G7CBp8o7L\"\n     onclick=\"log_download('arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023', 'https://openreview.net/forum?id=1G7CBp8o7L', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Adapting Neural Link Predictors for Data-Efficient Complex Query Answering [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.48550/arXiv.2301.12313\"\n     onclick=\"log_download('arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023', 'http://doi.org/10.48550/arXiv.2301.12313', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('arakelyan-minervini-daza-cochez-augenstein-adaptingneurallinkpredictorsfordataefficientcomplexqueryanswering-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{arakelyan_adapting_2023,\n\taddress = {New Orleans},\n\ttitle = {Adapting {Neural} {Link} {Predictors} for {Data}-{Efficient} {Complex} {Query} {Answering}},\n\turl = {https://openreview.net/forum?id=1G7CBp8o7L},\n\tdoi = {10.48550/arXiv.2301.12313},\n\tabstract = {Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD\n, a parameter-efficient score {\\textbackslash}emph\\{adaptation\\} model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component -- which only increases the number of model parameters by \n -- is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD\n produces significantly more accurate results than current state-of-the-art methods, improving from \n to \n Mean Reciprocal Rank values averaged across all datasets and query types while using \n of the available training query types. We further show that CQD\n is data-efficient, achieving competitive results with only \n of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.},\n\tpublisher = {OpenReview.net},\n\tauthor = {Arakelyan, Erik and Minervini, Pasquale and Daza, Daniel and Cochez, Michael and Augenstein, Isabelle},\n\tmonth = jul,\n\tyear = {2023},\n\tnote = {arXiv:2301.12313 [cs]},\n\tkeywords = {Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to address this problem by designing architectures trained end-to-end for the complex query answering task with a reasoning process that is hard to interpret while requiring data and resource-intensive training. Other lines of research have proposed re-using simple neural link predictors to answer complex queries, reducing the amount of training data by orders of magnitude while providing interpretable answers. The neural link predictor used in such approaches is not explicitly optimised for the complex query answering task, implying that its scores are not calibrated to interact together. We propose to address these problems via CQD , a parameter-efficient score \\emph\\adaptation\\ model optimised to re-calibrate neural link prediction scores for the complex query answering task. While the neural link predictor is frozen, the adaptation component – which only increases the number of model parameters by – is trained on the downstream complex query answering task. Furthermore, the calibration component enables us to support reasoning over queries that include atomic negations, which was previously impossible with link predictors. In our experiments, CQD produces significantly more accurate results than current state-of-the-art methods, improving from to Mean Reciprocal Rank values averaged across all datasets and query types while using of the available training query types. We further show that CQD is data-efficient, achieving competitive results with only of the complex training queries and robust in out-of-domain evaluations. Source code and datasets are available at https://github.com/EdinburghNLP/adaptive-cqd.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1145/3583780.3615294\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023', 'https://doi.org/10.1145/3583780.3615294', event);\">\n    Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Xiong, B.; Nayyeri, M.; Daza, D.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In Frommholz, I.; Hopfgartner, F.; Lee, M.; Oakes, M.; Lalmas, M.; Zhang, M.;  and Santos, R. L. T., editor(s), <i>Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023</i>, pages 5228–5231,  2023. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1145/3583780.3615294\"\n     onclick=\"log_download('xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023', 'https://doi.org/10.1145/3583780.3615294', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3583780.3615294\"\n     onclick=\"log_download('xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023', 'http://doi.org/10.1145/3583780.3615294', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('xiong-nayyeri-daza-cochez-reasoningbeyondtriplesrecentadvancesinknowledgegraphembeddings-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{xiong_reasoning_2023,\n\ttitle = {Reasoning beyond {Triples}: {Recent} {Advances} in {Knowledge} {Graph} {Embeddings}},\n\turl = {https://doi.org/10.1145/3583780.3615294},\n\tdoi = {10.1145/3583780.3615294},\n\tbooktitle = {Proceedings of the 32nd {ACM} {International} {Conference} on {Information} and {Knowledge} {Management}, {CIKM} 2023, {Birmingham}, {United} {Kingdom}, {October} 21-25, 2023},\n\tpublisher = {ACM},\n\tauthor = {Xiong, Bo and Nayyeri, Mojtaba and Daza, Daniel and Cochez, Michael},\n\teditor = {Frommholz, Ingo and Hopfgartner, Frank and Lee, Mark and Oakes, Michael and Lalmas, Mounia and Zhang, Min and Santos, Rodrygo L. T.},\n\tyear = {2023},\n\tpages = {5228--5231},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"scherp-richerby-blume-cochez-rau-structuralsummarizationofsemanticgraphsusingquotients-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Structural Summarization of Semantic Graphs Using Quotients.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Scherp, A.; Richerby, D.; Blume, T.; Cochez, M.;  and Rau, J.\n</span>\n\n  \n\n</span>\n\n  <i>Transactions on Graph Data and Knowledge</i>, 1(1). December 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/scherp-richerby-blume-cochez-rau-structuralsummarizationofsemanticgraphsusingquotients-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('scherp-richerby-blume-cochez-rau-structuralsummarizationofsemanticgraphsusingquotients-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{scherp_structural_2023,\n\ttitle = {Structural {Summarization} of {Semantic} {Graphs} {Using} {Quotients}},\n\tvolume = {1},\n\tabstract = {Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. \nWe consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. \n\nIn this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features.\nStructural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.},\n\tnumber = {1},\n\tjournal = {Transactions on Graph Data and Knowledge},\n\tauthor = {Scherp, Ansgar and Richerby, David and Blume, Till and Cochez, Michael and Rau, Jannik},\n\tmonth = dec,\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features. Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1186/s13326-023-00301-y\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023', 'https://doi.org/10.1186/s13326-023-00301-y', event);\">\n    BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Daza, D.; Alivanistos, D.; Mitra, P.; Pijnenburg, T.; Cochez, M.;  and Groth, P.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Biomedical Semantics</i>, 14(1): 20. December 2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1186/s13326-023-00301-y\"\n     onclick=\"log_download('daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023', 'https://doi.org/10.1186/s13326-023-00301-y', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1186/s13326-023-00301-y\"\n     onclick=\"log_download('daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023', 'http://doi.org/10.1186/s13326-023-00301-y', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('daza-alivanistos-mitra-pijnenburg-cochez-groth-bioblpamodularframeworkforlearningonmultimodalbiomedicalknowledgegraphs-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{daza_bioblp_2023,\n\ttitle = {{BioBLP}: a modular framework for learning on multimodal biomedical knowledge graphs},\n\tvolume = {14},\n\tissn = {2041-1480},\n\turl = {https://doi.org/10.1186/s13326-023-00301-y},\n\tdoi = {10.1186/s13326-023-00301-y},\n\tabstract = {Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain.},\n\tnumber = {1},\n\tjournal = {Journal of Biomedical Semantics},\n\tauthor = {Daza, Daniel and Alivanistos, Dimitrios and Mitra, Payal and Pijnenburg, Thom and Cochez, Michael and Groth, Paul},\n\tmonth = dec,\n\tyear = {2023},\n\tpages = {20},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Knowledge graphs (KGs) are an important tool for representing complex relationships between entities in the biomedical domain. Several methods have been proposed for learning embeddings that can be used to predict new links in such graphs. Some methods ignore valuable attribute data associated with entities in biomedical KGs, such as protein sequences, or molecular graphs. Other works incorporate such data, but assume that entities can be represented with the same data modality. This is not always the case for biomedical KGs, where entities exhibit heterogeneous modalities that are central to their representation in the subject domain.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/10.1007/978-3-031-43458-7_10\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023', 'https://link.springer.com/10.1007/978-3-031-43458-7_10', event);\">\n    Descriptive Comparison of Visual Ontology Change Summarisation Methods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chung, K.; Pernisch, R.;  and Schlobach, S.\n</span>\n\n  \n\n</span>\n\n  In Pesquita, C.; Skaf-Molli, H.; Efthymiou, V.; Kirrane, S.; Ngonga, A.; Collarana, D.; Cerqueira, R.; Alam, M.; Trojahn, C.;  and Hertling, S., editor(s), <i>The Semantic Web: ESWC 2023 Satellite Events</i>, volume 13998, pages 54–58, Cham,  2023. Springer Nature Switzerland\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/10.1007/978-3-031-43458-7_10\"\n     onclick=\"log_download('chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023', 'https://link.springer.com/10.1007/978-3-031-43458-7_10', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Descriptive Comparison of Visual Ontology Change Summarisation Methods [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-031-43458-7_10\"\n     onclick=\"log_download('chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023', 'http://doi.org/10.1007/978-3-031-43458-7_10', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chung-pernisch-schlobach-descriptivecomparisonofvisualontologychangesummarisationmethods-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pesquita_descriptive_2023,\n\taddress = {Cham},\n\ttitle = {Descriptive {Comparison} of {Visual} {Ontology} {Change} {Summarisation} {Methods}},\n\tvolume = {13998},\n\tisbn = {9783031434570 9783031434587},\n\turl = {https://link.springer.com/10.1007/978-3-031-43458-7_10},\n\tlanguage = {en},\n\turldate = {2023-11-13},\n\tbooktitle = {The {Semantic} {Web}: {ESWC} 2023 {Satellite} {Events}},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {Chung, Kornpol and Pernisch, Romana and Schlobach, Stefan},\n\teditor = {Pesquita, Catia and Skaf-Molli, Hala and Efthymiou, Vasilis and Kirrane, Sabrina and Ngonga, Axel and Collarana, Diego and Cerqueira, Renato and Alam, Mehwish and Trojahn, Cassia and Hertling, Sven},\n\tyear = {2023},\n\tdoi = {10.1007/978-3-031-43458-7_10},\n\tpages = {54--58},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"kim-cochez-francoislavet-neerincx-vossen-amachinewithhumanlikememorysystems-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  A Machine With Human-Like Memory Systems.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kim, T.; Cochez, M.; Francois-Lavet, V.; Neerincx, M.;  and Vossen, P.\n</span>\n\n  \n\n</span>\n\n  In <i>arXiv preprint arXiv:2204.01611</i>,  2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kim-cochez-francoislavet-neerincx-vossen-amachinewithhumanlikememorysystems-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kim-cochez-francoislavet-neerincx-vossen-amachinewithhumanlikememorysystems-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{kim_machine_2023,\n\ttitle = {A {Machine} {With} {Human}-{Like} {Memory} {Systems}},\n\tbooktitle = {{arXiv} preprint {arXiv}:2204.01611},\n\tauthor = {Kim, Taewoon and Cochez, Michael and Francois-Lavet, Vincent and Neerincx, Mark and Vossen, Piek},\n\tyear = {2023},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pal-lassance-djean-clinchant-parameterefficientsparseretrieversandrerankersusingadapters-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Parameter-Efficient Sparse Retrievers and Rerankers Using Adapters.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pal, V.; Lassance, C.; Déjean, H.;  and Clinchant, S.\n</span>\n\n  \n\n</span>\n\n  In Kamps, J.; Goeuriot, L.; Crestani, F.; Maistro, M.; Joho, H.; Davis, B.; Gurrin, C.; Kruschwitz, U.;  and Caputo, A., editor(s), <i>Advances in Information Retrieval</i>, of <i>Lecture Notes in Computer Science</i>, pages 16–31, Cham,  2023. Springer Nature Switzerland\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-031-28238-6_2\"\n     onclick=\"log_download('pal-lassance-djean-clinchant-parameterefficientsparseretrieversandrerankersusingadapters-2023', 'http://doi.org/10.1007/978-3-031-28238-6_2', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pal-lassance-djean-clinchant-parameterefficientsparseretrieversandrerankersusingadapters-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pal-lassance-djean-clinchant-parameterefficientsparseretrieversandrerankersusingadapters-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pal_parameter-efficient_2023,\n\taddress = {Cham},\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {Parameter-{Efficient} {Sparse} {Retrievers} and {Rerankers} {Using} {Adapters}},\n\tisbn = {978-3-031-28238-6},\n\tdoi = {10.1007/978-3-031-28238-6_2},\n\tabstract = {Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2\\% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade.)},\n\tlanguage = {en},\n\tbooktitle = {Advances in {Information} {Retrieval}},\n\tpublisher = {Springer Nature Switzerland},\n\tauthor = {Pal, Vaishali and Lassance, Carlos and Déjean, Hervé and Clinchant, Stéphane},\n\teditor = {Kamps, Jaap and Goeuriot, Lorraine and Crestani, Fabio and Maistro, Maria and Joho, Hideo and Davis, Brian and Gurrin, Cathal and Kruschwitz, Udo and Caputo, Annalina},\n\tyear = {2023},\n\tkeywords = {Adapters, Information Retrieval, Sparse neural retriever},\n\tpages = {16--31},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Parameter-Efficient transfer learning with Adapters have been studied in Natural Language Processing (NLP) as an alternative to full fine-tuning. Adapters are memory-efficient and scale well with downstream tasks by training small bottle-neck layers added between transformer layers while keeping the large pretrained language model (PLMs) frozen. In spite of showing promising results in NLP, these methods are under-explored in Information Retrieval. While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR. First, we study adapters for SPLADE, a sparse retriever, for which adapters not only retain the efficiency and effectiveness otherwise achieved by finetuning, but are memory-efficient and orders of magnitude lighter to train. We observe that Adapters-SPLADE not only optimizes just 2% of training parameters, but outperforms fully fine-tuned counterpart and existing parameter-efficient dense IR models on IR benchmark datasets. Secondly, we address domain adaptation of neural retrieval thanks to adapters on cross-domain BEIR datasets and TripClick. Finally, we also consider knowledge sharing between rerankers and first stage rankers. Overall, our study complete the examination of adapters for neural IR. (The code can be found at: https://github.com/naver/splade/tree/adapter-splade.)\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (17)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Towards Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Prodan, R.; Kimovski, D.; Bartolini, A.; Cochez, M.; Iosup, A.; Kharlamov, E.; Rožanec, J.; Vasiliu, L.;  and Vărbănescu, A. L.\n</span>\n\n  \n\n</span>\n\n  In <i>2022 IEEE Cloud Summit</i>, pages 23–30, October 2022. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/CloudSummit54781.2022.00010\"\n     onclick=\"log_download('prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022', 'http://doi.org/10.1109/CloudSummit54781.2022.00010', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('prodan-kimovski-bartolini-cochez-iosup-kharlamov-roanec-vasiliu-etal-towardsextremeandsustainablegraphprocessingforurgentsocietalchallengesineurope-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{prodan_towards_2022,\n\ttitle = {Towards {Extreme} and {Sustainable} {Graph} {Processing} for {Urgent} {Societal} {Challenges} in {Europe}},\n\tdoi = {10.1109/CloudSummit54781.2022.00010},\n\tabstract = {The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70\\% more efficient analytics than AliGraph, and 30 \\% improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 \\% lower greenhouse gas emissions for basic graph operations.},\n\tbooktitle = {2022 {IEEE} {Cloud} {Summit}},\n\tauthor = {Prodan, Radu and Kimovski, Dragi and Bartolini, Andrea and Cochez, Michael and Iosup, Alexandru and Kharlamov, Evgeny and Rožanec, Jože and Vasiliu, Laurenţiu and Vărbănescu, Ana Lucia},\n\tmonth = oct,\n\tyear = {2022},\n\tkeywords = {Cognition, Data centers, Europe, Extreme data, Green products, Serverless computing, Technological innovation, Transforms, graph processing, serverless computing, sustainability},\n\tpages = {23--30},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The Graph-Massivizer project, funded by the Horizon Europe research and innovation program, researches and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. It delivers a toolkit of five open-source software tools and FAIR graph datasets covering the sustainable lifecycle of processing extreme data as MGs. The tools focus on holistic usability (from extreme data ingestion and MG creation), automated intelligence (through analytics and reasoning), performance modelling, and environmental sustainability tradeoffs, supported by credible data-driven evidence across the computing continuum. The automated operation uses the emerging serverless computing paradigm for efficiency and event responsiveness. Thus, it supports experienced and novice stakeholders from a broad group of large and small organisations to capitalise on extreme data through MG programming and processing. Graph-Massivizer validates its innovation on four complementary use cases considering their extreme data properties and coverage of the three sustainability pillars (economy, society, and environment): sustainable green finance, global environment protection foresight, green AI for the sustainable automotive industry, and data centre digital twin for exascale computing. Graph-Massivizer promises 70% more efficient analytics than AliGraph, and 30 % improved energy awareness for extract, transform and load storage operations than Amazon Redshift. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25 % lower greenhouse gas emissions for basic graph operations.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3511808.3557366\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022', 'https://dl.acm.org/doi/10.1145/3511808.3557366', event);\">\n    Intersection of Parallels as an Early Stopping Criterion.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vardasbi, A.; de Rijke, M.;  and Dehghani, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 31st ACM International Conference on Information & Knowledge Management</i>, of <i>CIKM '22</i>, pages 1965–1974, New York, NY, USA, October 2022. Association for Computing Machinery\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3511808.3557366\"\n     onclick=\"log_download('vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022', 'https://dl.acm.org/doi/10.1145/3511808.3557366', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Intersection of Parallels as an Early Stopping Criterion [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3511808.3557366\"\n     onclick=\"log_download('vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022', 'http://doi.org/10.1145/3511808.3557366', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vardasbi-derijke-dehghani-intersectionofparallelsasanearlystoppingcriterion-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{vardasbi_intersection_2022,\n\taddress = {New York, NY, USA},\n\tseries = {{CIKM} &#x27;22},\n\ttitle = {Intersection of {Parallels} as an {Early} {Stopping} {Criterion}},\n\tisbn = {978-1-4503-9236-5},\n\turl = {https://dl.acm.org/doi/10.1145/3511808.3557366},\n\tdoi = {10.1145/3511808.3557366},\n\tabstract = {A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in information retrieval. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.},\n\turldate = {2023-04-21},\n\tbooktitle = {Proceedings of the 31st {ACM} {International} {Conference} on {Information} \\&amp; {Knowledge} {Management}},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Vardasbi, Ali and de Rijke, Maarten and Dehghani, Mostafa},\n\tmonth = oct,\n\tyear = {2022},\n\tkeywords = {cosine distance, early stopping, generalization, overparameterization},\n\tpages = {1965--1974},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  A common way to avoid overfitting in supervised learning is early stopping, where a held-out set is used for iterative evaluation during training to find a sweet spot in the number of training steps that gives maximum generalization. However, such a method requires a disjoint validation set, thus part of the labeled data from the training set is usually left out for this purpose, which is not ideal when training data is scarce. Furthermore, when the training labels are noisy, the performance of the model over a validation set may not be an accurate proxy for generalization. In this paper, we propose a method to spot an early stopping point in the training iterations of an overparameterized (NN) without the need for a validation set. We first show that in the overparameterized regime the randomly initialized weights of a linear model converge to the same direction during training. Using this result, we propose to train two parallel instances of a linear model, initialized with different random seeds, and use their intersection as a signal to detect overfitting. In order to detect intersection, we use the cosine distance between the weights of the parallel models during training iterations. Noticing that the final layer of a NN is a linear map of pre-last layer activations to output logits, we build on our criterion for linear models and propose an extension to multi-layer networks, using the new notion of counterfactual weights. We conduct experiments on two areas that early stopping has noticeable impact on preventing overfitting of a NN: (i) learning from noisy labels; and (ii) learning to rank in information retrieval. Our experiments on four widely used datasets confirm the effectiveness of our method for generalization. For a wide range of learning rates, our method, called Cosine-Distance Criterion (CDC), leads to better generalization on average than all the methods that we compare against in almost all of the tested cases.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1145/3477495.3532045\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022', 'https://doi.org/10.1145/3477495.3532045', event);\">\n    Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Vardasbi, A.; Sarvi, F.;  and de Rijke, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval</i>, of <i>SIGIR '22</i>, pages 715–725, New York, NY, USA, July 2022. Association for Computing Machinery\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1145/3477495.3532045\"\n     onclick=\"log_download('vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022', 'https://doi.org/10.1145/3477495.3532045', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3477495.3532045\"\n     onclick=\"log_download('vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022', 'http://doi.org/10.1145/3477495.3532045', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vardasbi-sarvi-derijke-probabilisticpermutationgraphsearchblackboxoptimizationforfairnessinranking-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{vardasbi_probabilistic_2022,\n\taddress = {New York, NY, USA},\n\tseries = {{SIGIR} &#x27;22},\n\ttitle = {Probabilistic {Permutation} {Graph} {Search}: {Black}-{Box} {Optimization} for {Fairness} in {Ranking}},\n\tisbn = {978-1-4503-8732-3},\n\tshorttitle = {Probabilistic {Permutation} {Graph} {Search}},\n\turl = {https://doi.org/10.1145/3477495.3532045},\n\tdoi = {10.1145/3477495.3532045},\n\tabstract = {There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. {\\textbackslash}acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, {\\textbackslash}acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to{\\textasciitilde}{\\textbackslash}acPL, our distribution representation, called{\\textasciitilde}{\\textbackslash}acPPG, can be used for black-box optimization of fairness. Different from{\\textasciitilde}{\\textbackslash}acPL, where pointwise logits are used as the distribution parameters, in{\\textasciitilde}{\\textbackslash}acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making{\\textasciitilde}{\\textbackslash}acPPG suitable for both deterministic and stochastic rankings. Our experiments show that{\\textasciitilde}{\\textbackslash}acPPG, while comparable to{\\textasciitilde}{\\textbackslash}acPL for larger session repetitions (i.e., stochastic ranking), improves over{\\textasciitilde}{\\textbackslash}acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of {\\textbackslash}acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as &quot;item \\$d\\_1\\$ should always be ranked higher than item \\$d\\_2\\$.&#x27;&#x27; Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.},\n\turldate = {2023-04-21},\n\tbooktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Vardasbi, Ali and Sarvi, Fatemeh and de Rijke, Maarten},\n\tmonth = jul,\n\tyear = {2022},\n\tkeywords = {fairness in ranking, permutation distribution, permutation graph, plackett-luce},\n\tpages = {715--725},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  There are several measures for fairness in ranking, based on different underlying assumptions and perspectives. \\acPL optimization with the REINFORCE algorithm can be used for optimizing black-box objective functions over permutations. In particular, it can be used for optimizing fairness measures. However, though effective for queries with a moderate number of repeating sessions, \\acPL optimization has room for improvement for queries with a small number of repeating sessions. In this paper, we present a novel way of representing permutation distributions, based on the notion of permutation graphs. Similar to~\\acPL, our distribution representation, called~\\acPPG, can be used for black-box optimization of fairness. Different from~\\acPL, where pointwise logits are used as the distribution parameters, in~\\acPPG pairwise inversion probabilities together with a reference permutation construct the distribution. As such, the reference permutation can be set to the best sampled permutation regarding the objective function, making~\\acPPG suitable for both deterministic and stochastic rankings. Our experiments show that~\\acPPG, while comparable to~\\acPL for larger session repetitions (i.e., stochastic ranking), improves over~\\acPL for optimizing fairness metrics for queries with one session (i.e., deterministic ranking). Additionally, when accurate utility estimations are available, e.g., in tabular models, the performance of \\acPPG in fairness optimization is significantly boosted compared to lower quality utility estimations from a learning to rank model, leading to a large performance gap with PL. Finally, the pairwise probabilities make it possible to impose pairwise constraints such as \"item $d_1$ should always be ranked higher than item $d_2$.'' Such constraints can be used to simultaneously optimize the fairness metric and control another objective such as ranking performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://content.iospress.com/articles/data-science/ds220056\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022', 'https://content.iospress.com/articles/data-science/ds220056', event);\">\n    Recommending scientific datasets using author networks in ensemble methods.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang, X.; van Harmelen, F.;  and Huang, Z.\n</span>\n\n  \n\n</span>\n\n  <i>Data Science</i>, 5(2): 167–193. July 2022.\n  <span class=\"note\">Publisher: IOS Press</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://content.iospress.com/articles/data-science/ds220056\"\n     onclick=\"log_download('wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022', 'https://content.iospress.com/articles/data-science/ds220056', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Recommending scientific datasets using author networks in ensemble methods [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3233/DS-220056\"\n     onclick=\"log_download('wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022', 'http://doi.org/10.3233/DS-220056', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-vanharmelen-huang-recommendingscientificdatasetsusingauthornetworksinensemblemethods-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{wang_recommending_2022,\n\ttitle = {Recommending scientific datasets using author networks in ensemble methods},\n\tvolume = {5},\n\tissn = {2451-8484},\n\turl = {https://content.iospress.com/articles/data-science/ds220056},\n\tdoi = {10.3233/DS-220056},\n\tabstract = {Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation : the task of re},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2023-04-22},\n\tjournal = {Data Science},\n\tauthor = {Wang, Xu and van Harmelen, Frank and Huang, Zhisheng},\n\tmonth = jul,\n\tyear = {2022},\n\tnote = {Publisher: IOS Press},\n\tpages = {167--193},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation : the task of re\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"xiong-cochez-nayyeri-staab-hyperbolicembeddinginferenceforstructuredmultilabelprediction-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Hyperbolic Embedding Inference for Structured Multi-Label Prediction.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Xiong, B.; Cochez, M.; Nayyeri, M.;  and Staab, S.\n</span>\n\n  \n\n</span>\n\n  In <i>NeurIPS2022</i>, November 2022. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/xiong-cochez-nayyeri-staab-hyperbolicembeddinginferenceforstructuredmultilabelprediction-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('xiong-cochez-nayyeri-staab-hyperbolicembeddinginferenceforstructuredmultilabelprediction-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{xiong_hyperbolic_2022,\n\ttitle = {Hyperbolic {Embedding} {Inference} for {Structured} {Multi}-{Label} {Prediction}},\n\tbooktitle = {{NeurIPS2022}},\n\tauthor = {Xiong, Bo and Cochez, Michael and Nayyeri, Mojtaba and Staab, Steffen},\n\tmonth = nov,\n\tyear = {2022},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/978-3-031-17105-5\\_4\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022', 'https://doi.org/10.1007/978-3-031-17105-5\\_4', event);\">\n    Question Answering with Additive Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Harper, C. A.; Jr, R. D.;  and Groth, P.\n</span>\n\n  \n\n</span>\n\n  In Corcho, Ó.; Hollink, L.; Kutz, O.; Troquard, N.;  and Ekaputra, F. J., editor(s), <i>Knowledge Engineering and Knowledge Management - 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26-29, 2022, Proceedings</i>, volume 13514, of <i>Lecture Notes in Computer Science</i>, pages 51–65, September 2022. Springer\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/978-3-031-17105-5\\_4\"\n     onclick=\"log_download('harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022', 'https://doi.org/10.1007/978-3-031-17105-5\\_4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Question Answering with Additive Restrictive Training (QuAART): Question Answering for the Rapid Development of New Knowledge Extraction Pipelines [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-031-17105-5_4\"\n     onclick=\"log_download('harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022', 'http://doi.org/10.1007/978-3-031-17105-5_4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('harper-jr-groth-questionansweringwithadditiverestrictivetrainingquaartquestionansweringfortherapiddevelopmentofnewknowledgeextractionpipelines-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{harper_question_2022,\n\tseries = {Lecture {Notes} in {Computer} {Science}},\n\ttitle = {Question {Answering} with {Additive} {Restrictive} {Training} ({QuAART}): {Question} {Answering} for the {Rapid} {Development} of {New} {Knowledge} {Extraction} {Pipelines}},\n\tvolume = {13514},\n\turl = {https://doi.org/10.1007/978-3-031-17105-5\\_4},\n\tdoi = {10.1007/978-3-031-17105-5_4},\n\tbooktitle = {Knowledge {Engineering} and {Knowledge} {Management} - 23rd {International} {Conference}, {EKAW} 2022, {Bolzano}, {Italy}, {September} 26-29, 2022, {Proceedings}},\n\tpublisher = {Springer},\n\tauthor = {Harper, Corey A. and Jr, Ron Daniel and Groth, Paul},\n\teditor = {Corcho, Óscar and Hollink, Laura and Kutz, Oliver and Troquard, Nicolas and Ekaputra, Fajar J.},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {51--65},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-vanharmelen-cochez-huang-yang-kong-zhang-qiu-scientificitemrecommendationusingacitationnetwork-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Scientific Item Recommendation Using a Citation Network.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang, X.; van Harmelen, F.; Cochez, M.; Huang, Z.; Yang, B.; Kong, L.; Zhang, T.;  and Qiu, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Knowledge Science, Engineering and Management</i>, pages 469–484, Cham, August 2022. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-vanharmelen-cochez-huang-yang-kong-zhang-qiu-scientificitemrecommendationusingacitationnetwork-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-vanharmelen-cochez-huang-yang-kong-zhang-qiu-scientificitemrecommendationusingacitationnetwork-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{wang_scientific_2022,\n\taddress = {Cham},\n\ttitle = {Scientific {Item} {Recommendation} {Using} a {Citation} {Network}},\n\tisbn = {978-3-031-10986-7},\n\tbooktitle = {Knowledge {Science}, {Engineering} and {Management}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Wang, Xu and van Harmelen, Frank and Cochez, Michael and Huang, Zhisheng&quot;, editor=&quot;Memmi, Gerard and Yang, Baijian and Kong, Linghe and Zhang, Tianwei and Qiu, Meikang},\n\tmonth = aug,\n\tyear = {2022},\n\tpages = {469--484},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"barthelemy-cochez-dimitriadis-karim-loutas-magnisalis-comet-peristeras-etal-towardsastandardbasedopendataecosystemanalysisofdcatapuseatnationalandeuropeanlevel-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Towards a standard-based open data ecosystem: analysis of DCAT-AP use at national and European level.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Barthelemy, F.; Cochez, M.; Dimitriadis, I.; Karim, N.; Loutas, N.; Magnisalis, I.; Comet, L. M.; Peristeras, V.;  and Wyns, B.\n</span>\n\n  \n\n</span>\n\n  <i>Electronic Government, an International Journal</i>, 18(2): 137–180. January 2022.\n  <span class=\"note\">Publisher: Inderscience Publishers (IEL)</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/barthelemy-cochez-dimitriadis-karim-loutas-magnisalis-comet-peristeras-etal-towardsastandardbasedopendataecosystemanalysisofdcatapuseatnationalandeuropeanlevel-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('barthelemy-cochez-dimitriadis-karim-loutas-magnisalis-comet-peristeras-etal-towardsastandardbasedopendataecosystemanalysisofdcatapuseatnationalandeuropeanlevel-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{barthelemy_towards_2022,\n\ttitle = {Towards a standard-based open data ecosystem: analysis of {DCAT}-{AP} use at national and {European} level},\n\tvolume = {18},\n\tnumber = {2},\n\tjournal = {Electronic Government, an International Journal},\n\tauthor = {Barthelemy, Florian and Cochez, Michael and Dimitriadis, Iraklis and Karim, Naila and Loutas, Nikolaos and Magnisalis, Ioannis and Comet, Lina Molinas and Peristeras, Vassilios and Wyns, Brecht},\n\tmonth = jan,\n\tyear = {2022},\n\tnote = {Publisher: Inderscience Publishers (IEL)},\n\tpages = {137--180},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2203.02622\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022', 'https://arxiv.org/abs/2203.02622', event);\">\n    Scaling R-GCN Training with Graph Summarization.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Generale, A.; Blume, T.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In <i>WWW '22: Companion Proceedings of the Web Conference 2022</i>, of <i>WWW '22</i>, pages 1073–1082, Virtual Event, Lyon, France, April 2022. Association for Computing Machinery\n  <span class=\"note\">arXiv preprint arXiv:2203.02622</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2203.02622\"\n     onclick=\"log_download('generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022', 'https://arxiv.org/abs/2203.02622', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scaling R-GCN Training with Graph Summarization [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1145/3487553.3524719\"\n     onclick=\"log_download('generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022', 'http://doi.org/https://doi.org/10.1145/3487553.3524719', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('generale-blume-cochez-scalingrgcntrainingwithgraphsummarization-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{generale_scaling_2022,\n\taddress = {Virtual Event, Lyon, France},\n\tseries = {{WWW} &#x27;22},\n\ttitle = {Scaling {R}-{GCN} {Training} with {Graph} {Summarization}},\n\tisbn = {978-1-4503-9130-6},\n\turl = {https://arxiv.org/abs/2203.02622},\n\tdoi = {https://doi.org/10.1145/3487553.3524719},\n\tbooktitle = {{WWW} &#x27;22: {Companion} {Proceedings} of the {Web} {Conference} 2022},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Generale, Alessandro and Blume, Till and Cochez, Michael},\n\tmonth = apr,\n\tyear = {2022},\n\tnote = {arXiv preprint arXiv:2203.02622},\n\tkeywords = {graph neural network, graph summarization, scalability},\n\tpages = {1073--1082},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2209.01665\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022', 'https://arxiv.org/abs/2209.01665', event);\">\n    Exposure-Aware Recommendation using Contextual Bandits.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mansoury, M.; Mobasher, B.;  and van Hoof, H.\n</span>\n\n  \n\n</span>\n\n  In September 2022. 5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2209.01665\"\n     onclick=\"log_download('mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022', 'https://arxiv.org/abs/2209.01665', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Exposure-Aware Recommendation using Contextual Bandits [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.48550/ARXIV.2209.01665\"\n     onclick=\"log_download('mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022', 'http://doi.org/10.48550/ARXIV.2209.01665', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mansoury-mobasher-vanhoof-exposureawarerecommendationusingcontextualbandits-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mansoury_exposure-aware_2022,\n\ttitle = {Exposure-{Aware} {Recommendation} using {Contextual} {Bandits}},\n\tcopyright = {Creative Commons Attribution 4.0 International},\n\turl = {https://arxiv.org/abs/2209.01665},\n\tdoi = {10.48550/ARXIV.2209.01665},\n\tpublisher = {5th FAccTRec Workshop on Responsible Recommendation in conjunction with ACM RecSys 2022},\n\tauthor = {Mansoury, Masoud and Mobasher, Bamshad and van Hoof, Herke},\n\tmonth = sep,\n\tyear = {2022},\n\tkeywords = {FOS: Computer and information sciences, Information Retrieval (cs.IR)},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=4rLw09TgRw9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022', 'https://openreview.net/forum?id=4rLw09TgRw9', event);\">\n    Query Embedding on Hyper-Relational Knowledge Graphs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Alivanistos, D.; Berrendorf, M.; Cochez, M.;  and Galkin, M.\n</span>\n\n  \n\n</span>\n\n  In <i>The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022</i>, April 2022. OpenReview.net\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=4rLw09TgRw9\"\n     onclick=\"log_download('alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022', 'https://openreview.net/forum?id=4rLw09TgRw9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Query Embedding on Hyper-Relational Knowledge Graphs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('alivanistos-berrendorf-cochez-galkin-queryembeddingonhyperrelationalknowledgegraphs-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{alivanistos_query_2022,\n\ttitle = {Query {Embedding} on {Hyper}-{Relational} {Knowledge} {Graphs}},\n\turl = {https://openreview.net/forum?id=4rLw09TgRw9},\n\tbooktitle = {The {Tenth} {International} {Conference} on {Learning} {Representations}, {ICLR} 2022, {Virtual} {Event}, {April} 25-29, 2022},\n\tpublisher = {OpenReview.net},\n\tauthor = {Alivanistos, Dimitrios and Berrendorf, Max and Cochez, Michael and Galkin, Mikhail},\n\tmonth = apr,\n\tyear = {2022},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://ceur-ws.org/Vol-3121/short2.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022', 'http://ceur-ws.org/Vol-3121/short2.pdf', event);\">\n    Potential Energy to Improve Link Prediction With Relational Graph Neural Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Colombo, S.; Alivanistos, D.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In Martin, A.; Hinkelmann, K.; Fill, H.; Gerber, A.; Lenat, D.; Stolle, R.;  and Harmelen, F. v., editor(s), <i>Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022</i>, volume 3121, of <i>CEUR Workshop Proceedings</i>, March 2022. CEUR-WS.org\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://ceur-ws.org/Vol-3121/short2.pdf\"\n     onclick=\"log_download('colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022', 'http://ceur-ws.org/Vol-3121/short2.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Potential Energy to Improve Link Prediction With Relational Graph Neural Networks [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('colombo-alivanistos-cochez-potentialenergytoimprovelinkpredictionwithrelationalgraphneuralnetworks-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{colombo_potential_2022,\n\tseries = {{CEUR} {Workshop} {Proceedings}},\n\ttitle = {Potential {Energy} to {Improve} {Link} {Prediction} {With} {Relational} {Graph} {Neural} {Networks}},\n\tvolume = {3121},\n\turl = {http://ceur-ws.org/Vol-3121/short2.pdf},\n\tbooktitle = {Proceedings of the {AAAI} 2022 {Spring} {Symposium} on {Machine} {Learning} and {Knowledge} {Engineering} for {Hybrid} {Intelligence} ({AAAI}-{MAKE} 2022), {Stanford} {University}, {Palo} {Alto}, {California}, {USA}, {March} 21-23, 2022},\n\tpublisher = {CEUR-WS.org},\n\tauthor = {Colombo, Simone and Alivanistos, Dimitrios and Cochez, Michael},\n\teditor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and Harmelen, Frank van},\n\tmonth = mar,\n\tyear = {2022},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ceur-ws.org/Vol-3274/paper2.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022', 'https://ceur-ws.org/Vol-3274/paper2.pdf', event);\">\n    Prompting as Probing: Using Language Models for Knowledge Base Construction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Alivanistos, D.; Santamaría, S. B.; Cochez, M.; Kalo, J.; van Krieken, E.;  and Thanapalasingam, T.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Semantic Web Challenge on Knowledge Base Construction from Pre-trained Language Models 2022 co-located with the 21st International Semantic Web Conference (ISWC2022)</i>, volume 3274, pages 11–34, October 2022. CEUR-ws.org\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ceur-ws.org/Vol-3274/paper2.pdf\"\n     onclick=\"log_download('alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022', 'https://ceur-ws.org/Vol-3274/paper2.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Prompting as Probing: Using Language Models for Knowledge Base Construction [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.48550/ARXIV.2208.11057\"\n     onclick=\"log_download('alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022', 'http://doi.org/10.48550/ARXIV.2208.11057', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('alivanistos-santamara-cochez-kalo-vankrieken-thanapalasingam-promptingasprobingusinglanguagemodelsforknowledgebaseconstruction-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{alivanistos_prompting_2022,\n\ttitle = {Prompting as {Probing}: {Using} {Language} {Models} for {Knowledge} {Base} {Construction}},\n\tvolume = {3274},\n\tcopyright = {arXiv.org perpetual, non-exclusive license},\n\turl = {https://ceur-ws.org/Vol-3274/paper2.pdf},\n\tdoi = {10.48550/ARXIV.2208.11057},\n\tbooktitle = {Proceedings of the {Semantic} {Web} {Challenge} on {Knowledge} {Base} {Construction} from {Pre}-trained {Language} {Models} 2022 co-located with the 21st {International} {Semantic} {Web} {Conference} ({ISWC2022})},\n\tpublisher = {CEUR-ws.org},\n\tauthor = {Alivanistos, Dimitrios and Santamaría, Selene Báez and Cochez, Michael and Kalo, Jan-Christoph and van Krieken, Emile and Thanapalasingam, Thiviyan},\n\tmonth = oct,\n\tyear = {2022},\n\tkeywords = {Artificial Intelligence (cs.AI), Computation and Language (cs.CL), FOS: Computer and information sciences},\n\tpages = {11--34},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3477495.3531700\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022', 'https://dl.acm.org/doi/10.1145/3477495.3531700', event);\">\n    The Seventh Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI'22).\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Penha, G.; Vakulenko, S.; Dusek, O.; Clark, L.; Pal, V.;  and Adlakha, V.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval</i>, of <i>SIGIR '22</i>, pages 3466–3469, New York, NY, USA, July 2022. Association for Computing Machinery\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3477495.3531700\"\n     onclick=\"log_download('penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022', 'https://dl.acm.org/doi/10.1145/3477495.3531700', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"The Seventh Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI&#x27;22) [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3477495.3531700\"\n     onclick=\"log_download('penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022', 'http://doi.org/10.1145/3477495.3531700', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('penha-vakulenko-dusek-clark-pal-adlakha-theseventhworkshoponsearchorientedconversationalartificialintelligencescai22-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{penha_seventh_2022,\n\taddress = {New York, NY, USA},\n\tseries = {{SIGIR} &#x27;22},\n\ttitle = {The {Seventh} {Workshop} on {Search}-{Oriented} {Conversational} {Artificial} {Intelligence} ({SCAI}&#x27;22)},\n\tisbn = {978-1-4503-8732-3},\n\turl = {https://dl.acm.org/doi/10.1145/3477495.3531700},\n\tdoi = {10.1145/3477495.3531700},\n\tabstract = {The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.},\n\turldate = {2023-04-20},\n\tbooktitle = {Proceedings of the 45th {International} {ACM} {SIGIR} {Conference} on {Research} and {Development} in {Information} {Retrieval}},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Penha, Gustavo and Vakulenko, Svitlana and Dusek, Ondrej and Clark, Leigh and Pal, Vaishali and Adlakha, Vaibhav},\n\tmonth = jul,\n\tyear = {2022},\n\tkeywords = {conversational information access, conversational search, information-seeking dialogue},\n\tpages = {3466--3469},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The goal of the seventh edition of SCAI (https://scai.info) is to bring together and further grow a community of researchers and practitioners interested in conversational systems for information access. The previous iterations of the workshop already demonstrated the breadth and multidisciplinarity inherent in the design and development of conversational search agents. The proposed shift from traditional web search to search interfaces enabled via human-like dialogue leads to a number of challenges, and although such challenges have received more attention in the recent years, there are many pending research questions that should be addressed by the information retrieval community and can largely benefit from a collaboration with other research fields, such as natural language processing, machine learning, human-computer interaction and dialogue systems. This workshop is intended as a platform enabling a continuous discussion of the major research challenges that surround the design of search-oriented conversational systems. This year, participants have the opportunity to meet in person and have more in-depth interactive discussions with a full-day onsite workshop.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3523227.3547410\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022', 'https://dl.acm.org/doi/10.1145/3523227.3547410', event);\">\n    MORS 2022: The Second Workshop on Multi-Objective Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Abdollahpouri, H.; Sahebi, S.; Elahi, M.; Mansoury, M.; Loni, B.; Nazari, Z.;  and Dimakopoulou, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Sixteenth ACM Conference on Recommender Systems</i>, pages 658–660, Seattle WA USA, September 2022. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3523227.3547410\"\n     onclick=\"log_download('abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022', 'https://dl.acm.org/doi/10.1145/3523227.3547410', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"MORS 2022: The Second Workshop on Multi-Objective Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3523227.3547410\"\n     onclick=\"log_download('abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022', 'http://doi.org/10.1145/3523227.3547410', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('abdollahpouri-sahebi-elahi-mansoury-loni-nazari-dimakopoulou-mors2022thesecondworkshoponmultiobjectiverecommendersystems-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{abdollahpouri_mors_2022,\n\taddress = {Seattle WA USA},\n\ttitle = {{MORS} 2022: {The} {Second} {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},\n\tisbn = {978-1-4503-9278-5},\n\tshorttitle = {{MORS} 2022},\n\turl = {https://dl.acm.org/doi/10.1145/3523227.3547410},\n\tdoi = {10.1145/3523227.3547410},\n\tlanguage = {en},\n\turldate = {2023-04-20},\n\tbooktitle = {Sixteenth {ACM} {Conference} on {Recommender} {Systems}},\n\tpublisher = {ACM},\n\tauthor = {Abdollahpouri, Himan and Sahebi, Shaghayegh and Elahi, Mehdi and Mansoury, Masoud and Loni, Babak and Nazari, Zahra and Dimakopoulou, Maria},\n\tmonth = sep,\n\tyear = {2022},\n\tpages = {658--660},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://aclanthology.org/2022.spnlp-1.4\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022', 'https://aclanthology.org/2022.spnlp-1.4', event);\">\n    SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Daza, D.; Cochez, M.;  and Groth, P.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Sixth Workshop on Structured Prediction for NLP</i>, pages 32–39, Dublin, Ireland, May 2022. Association for Computational Linguistics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://aclanthology.org/2022.spnlp-1.4\"\n     onclick=\"log_download('daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022', 'https://aclanthology.org/2022.spnlp-1.4', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SlotGAN: Detecting Mentions in Text via Adversarial Distant Learning [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.18653/v1/2022.spnlp-1.4\"\n     onclick=\"log_download('daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022', 'http://doi.org/10.18653/v1/2022.spnlp-1.4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('daza-cochez-groth-slotgandetectingmentionsintextviaadversarialdistantlearning-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{daza_slotgan_2022,\n\taddress = {Dublin, Ireland},\n\ttitle = {{SlotGAN}: {Detecting} {Mentions} in {Text} via {Adversarial} {Distant} {Learning}},\n\turl = {https://aclanthology.org/2022.spnlp-1.4},\n\tdoi = {10.18653/v1/2022.spnlp-1.4},\n\tabstract = {We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95\\% of the precision of a supervised method, and 84\\% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.},\n\tbooktitle = {Proceedings of the {Sixth} {Workshop} on {Structured} {Prediction} for {NLP}},\n\tpublisher = {Association for Computational Linguistics},\n\tauthor = {Daza, Daniel and Cochez, Michael and Groth, Paul},\n\tmonth = may,\n\tyear = {2022},\n\tpages = {32--39},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We present SlotGAN, a framework for training a mention detection model that only requires unlabeled text and a gazetteer. It consists of a generator trained to extract spans from an input sentence, and a discriminator trained to determine whether a span comes from the generator, or from the gazetteer.We evaluate the method on English newswire data and compare it against supervised, weakly-supervised, and unsupervised methods. We find that the performance of the method is lower than these baselines, because it tends to generate more and longer spans, and in some cases it relies only on capitalization. In other cases, it generates spans that are valid but differ from the benchmark. When evaluated with metrics based on overlap, we find that SlotGAN performs within 95% of the precision of a supervised method, and 84% of its recall. Our results suggest that the model can generate spans that overlap well, but an additional filtering mechanism is required.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1570826822000117\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022', 'https://www.sciencedirect.com/science/article/pii/S1570826822000117', event);\">\n    Visualising the effects of ontology changes and studying their understanding with ChImp.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pernisch, R.; Dell’Aglio, D.; Serbak, M.; Gonçalves, R. S.;  and Bernstein, A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Web Semantics</i>, 74: 100715. October 2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S1570826822000117\"\n     onclick=\"log_download('pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022', 'https://www.sciencedirect.com/science/article/pii/S1570826822000117', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Visualising the effects of ontology changes and studying their understanding with ChImp [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.websem.2022.100715\"\n     onclick=\"log_download('pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022', 'http://doi.org/10.1016/j.websem.2022.100715', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>7 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pernisch-dellaglio-serbak-gonalves-bernstein-visualisingtheeffectsofontologychangesandstudyingtheirunderstandingwithchimp-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{pernisch_visualising_2022,\n\ttitle = {Visualising the effects of ontology changes and studying their understanding with {ChImp}},\n\tvolume = {74},\n\tissn = {1570-8268},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1570826822000117},\n\tdoi = {10.1016/j.websem.2022.100715},\n\tabstract = {Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.},\n\tlanguage = {en},\n\turldate = {2022-06-20},\n\tjournal = {Journal of Web Semantics},\n\tauthor = {Pernisch, Romana and Dell’Aglio, Daniele and Serbak, Mirko and Gonçalves, Rafael S. and Bernstein, Abraham},\n\tmonth = oct,\n\tyear = {2022},\n\tkeywords = {Materialisation, Ontology editing, Ontology evolution impact, User study},\n\tpages = {100715},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (11)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.cochez.nl/papers/feature_selection_for_exploration.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021', 'https://www.cochez.nl/papers/feature_selection_for_exploration.pdf', event);\">\n    Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Chakrabarti, A.; Das, A.; Cochez, M.;  and Quix, C.\n</span>\n\n  \n\n</span>\n\n  In Bellatreche, L.; Dumas, M.; Karras, P.;  and Matulevičius, R., editor(s), <i>Advances in Databases and Information Systems</i>, pages 183–197, Cham, August 2021. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.cochez.nl/papers/feature_selection_for_exploration.pdf\"\n     onclick=\"log_download('chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021', 'https://www.cochez.nl/papers/feature_selection_for_exploration.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Unsupervised Feature Selection for Efficient Exploration of High Dimensional Data [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('chakrabarti-das-cochez-quix-unsupervisedfeatureselectionforefficientexplorationofhighdimensionaldata-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{chakrabarti_unsupervised_2021,\n\taddress = {Cham},\n\ttitle = {Unsupervised {Feature} {Selection} for {Efficient} {Exploration} of {High} {Dimensional} {Data}},\n\tisbn = {978-3-030-82472-3},\n\turl = {https://www.cochez.nl/papers/feature_selection_for_exploration.pdf},\n\tabstract = {The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.},\n\tbooktitle = {Advances in {Databases} and {Information} {Systems}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Chakrabarti, Arnab and Das, Abhijeet and Cochez, Michael and Quix, Christoph},\n\teditor = {Bellatreche, Ladjel and Dumas, Marlon and Karras, Panagiotis and Matulevičius, Raimundas},\n\tmonth = aug,\n\tyear = {2021},\n\tpages = {183--197},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The exponential growth in the ability to generate, capture, and store high dimensional data has driven sophisticated machine learning applications. However, high dimensionality often poses a challenge for analysts to effectively identify and extract relevant features from datasets. Though many feature selection methods have shown good results in supervised learning, the major challenge lies in the area of unsupervised feature selection. For example, in the domain of data visualization, high-dimensional data is difficult to visualize and interpret due to the limitations of the screen, resulting in visual clutter. Visualizations are more interpretable when visualized in a low dimensional feature space. To mitigate these challenges, we present an approach to perform unsupervised feature clustering and selection using our novel graph clustering algorithm based on Clique-Cover Theory. We implemented our approach in an interactive data exploration tool which facilitates the exploration of relationships between features and generates interpretable visualizations.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karim-jiao-dhmen-cochez-beyan-rebholzschuhmann-decker-deepkneeexplainerexplainablekneeosteoarthritisdiagnosisfromradiographsandmagneticresonanceimaging-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis From Radiographs and Magnetic Resonance Imaging.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karim, M. R.; Jiao, J.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>, 9: 39757–39780. February 2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACCESS.2021.3062493\"\n     onclick=\"log_download('karim-jiao-dhmen-cochez-beyan-rebholzschuhmann-decker-deepkneeexplainerexplainablekneeosteoarthritisdiagnosisfromradiographsandmagneticresonanceimaging-2021', 'http://doi.org/10.1109/ACCESS.2021.3062493', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karim-jiao-dhmen-cochez-beyan-rebholzschuhmann-decker-deepkneeexplainerexplainablekneeosteoarthritisdiagnosisfromradiographsandmagneticresonanceimaging-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karim-jiao-dhmen-cochez-beyan-rebholzschuhmann-decker-deepkneeexplainerexplainablekneeosteoarthritisdiagnosisfromradiographsandmagneticresonanceimaging-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{karim_deepkneeexplainer_2021,\n\ttitle = {{DeepKneeExplainer}: {Explainable} {Knee} {Osteoarthritis} {Diagnosis} {From} {Radiographs} and {Magnetic} {Resonance} {Imaging}},\n\tvolume = {9},\n\tdoi = {10.1109/ACCESS.2021.3062493},\n\tjournal = {IEEE Access},\n\tauthor = {Karim, Md. Rezaul and Jiao, Jiao and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\n\tmonth = feb,\n\tyear = {2021},\n\tpages = {39757--39780},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"vanbakel-aleksiev-daza-alivanistos-cochez-approximateknowledgegraphqueryansweringfromrankingtobinaryclassification-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  van Bakel, R.; Aleksiev, T.; Daza, D.; Alivanistos, D.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In Cochez, M.; Croitoru, M.; Marquis, P.;  and Rudolph, S., editor(s), <i>Graph Structures for Knowledge Representation and Reasoning</i>, pages 107–124, Cham, September 2021. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/vanbakel-aleksiev-daza-alivanistos-cochez-approximateknowledgegraphqueryansweringfromrankingtobinaryclassification-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('vanbakel-aleksiev-daza-alivanistos-cochez-approximateknowledgegraphqueryansweringfromrankingtobinaryclassification-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{van_bakel_approximate_2021,\n\taddress = {Cham},\n\ttitle = {Approximate {Knowledge} {Graph} {Query} {Answering}: {From} {Ranking} to {Binary} {Classification}},\n\tisbn = {978-3-030-72308-8},\n\tabstract = {Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.},\n\tbooktitle = {Graph {Structures} for {Knowledge} {Representation} and {Reasoning}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {van Bakel, Ruud and Aleksiev, Teodor and Daza, Daniel and Alivanistos, Dimitrios and Cochez, Michael},\n\teditor = {Cochez, Michael and Croitoru, Madalina and Marquis, Pierre and Rudolph, Sebastian},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {107--124},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=PoAI5RrlUFj\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021', 'https://openreview.net/forum?id=PoAI5RrlUFj', event);\">\n    Towards Easy Vocabulary Drafts with Neologism 2.0.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lipp, J.; Gleim, L.; Cochez, M.; Dimitriadis, I.; Ali, H.; Alvarez, D. H.; Lange, C.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  In Verborgh, R.; Dimou, A.; Hogan, A.; d'Amato , C.; Tiddi, I.; Bröring, A.; Mayer, S.; Ongenae, F.; Tommasini, R.;  and Alam, M., editor(s), <i>The Semantic Web: ESWC 2021 Satellite Events</i>, pages 21–26, Cham, June 2021. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=PoAI5RrlUFj\"\n     onclick=\"log_download('lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021', 'https://openreview.net/forum?id=PoAI5RrlUFj', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards Easy Vocabulary Drafts with Neologism 2.0 [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lipp-gleim-cochez-dimitriadis-ali-alvarez-lange-decker-towardseasyvocabularydraftswithneologism20-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{lipp_towards_2021,\n\taddress = {Cham},\n\ttitle = {Towards {Easy} {Vocabulary} {Drafts} with {Neologism} 2.0},\n\tisbn = {978-3-030-80418-3},\n\turl = {https://openreview.net/forum?id=PoAI5RrlUFj},\n\tabstract = {Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.},\n\tbooktitle = {The {Semantic} {Web}: {ESWC} 2021 {Satellite} {Events}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Lipp, Johannes and Gleim, Lars and Cochez, Michael and Dimitriadis, Iraklis and Ali, Hussain and Alvarez, Daniel Hoppe and Lange, Christoph and Decker, Stefan},\n\teditor = {Verborgh, Ruben and Dimou, Anastasia and Hogan, Aidan and d&#x27;Amato, Claudia and Tiddi, Ilaria and Bröring, Arne and Mayer, Simon and Ongenae, Femke and Tommasini, Riccardo and Alam, Mehwish},\n\tmonth = jun,\n\tyear = {2021},\n\tkeywords = {discoverylab},\n\tpages = {21--26},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1145/3442381.3450141\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021', 'https://doi.org/10.1145/3442381.3450141', event);\">\n    Inductive Entity Representations from Text via Link Prediction.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Daza, D.; Cochez, M.;  and Groth, P.\n</span>\n\n  \n\n</span>\n\n  In Leskovec, J.; Grobelnik, M.; Najork, M.; Tang, J.;  and Zia, L., editor(s), <i>WWW '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021</i>, pages 798–808, April 2021. ACM / IW3C2\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1145/3442381.3450141\"\n     onclick=\"log_download('daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021', 'https://doi.org/10.1145/3442381.3450141', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Inductive Entity Representations from Text via Link Prediction [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3442381.3450141\"\n     onclick=\"log_download('daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021', 'http://doi.org/10.1145/3442381.3450141', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('daza-cochez-groth-inductiveentityrepresentationsfromtextvialinkprediction-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{daza_inductive_2021,\n\ttitle = {Inductive {Entity} {Representations} from {Text} via {Link} {Prediction}},\n\turl = {https://doi.org/10.1145/3442381.3450141},\n\tdoi = {10.1145/3442381.3450141},\n\tbooktitle = {{WWW} &#x27;21: {The} {Web} {Conference} 2021, {Virtual} {Event} / {Ljubljana}, {Slovenia}, {April} 19-23, 2021},\n\tpublisher = {ACM / IW3C2},\n\tauthor = {Daza, Daniel and Cochez, Michael and Groth, Paul},\n\teditor = {Leskovec, Jure and Grobelnik, Marko and Najork, Marc and Tang, Jie and Zia, Leila},\n\tmonth = apr,\n\tyear = {2021},\n\tpages = {798--808},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://openreview.net/forum?id=Mos9F9kDwkz\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021', 'https://openreview.net/forum?id=Mos9F9kDwkz', event);\">\n    Complex Query Answering with Neural Link Predictors.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Arakelyan, E.; Daza, D.; Minervini, P.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In <i>International Conference on Learning Representations (ICLR 2021)</i>, May 2021. Openreview\n  <span class=\"note\">_eprint: 2011.03459</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://openreview.net/forum?id=Mos9F9kDwkz\"\n     onclick=\"log_download('arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021', 'https://openreview.net/forum?id=Mos9F9kDwkz', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Complex Query Answering with Neural Link Predictors [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>5 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('arakelyan-daza-minervini-cochez-complexqueryansweringwithneurallinkpredictors-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{arakelyan_complex_2021,\n\ttitle = {Complex {Query} {Answering} with {Neural} {Link} {Predictors}},\n\turl = {https://openreview.net/forum?id=Mos9F9kDwkz},\n\tbooktitle = {International {Conference} on {Learning} {Representations} ({ICLR} 2021)},\n\tpublisher = {Openreview},\n\tauthor = {Arakelyan, Erik and Daza, Daniel and Minervini, Pasquale and Cochez, Michael},\n\tmonth = may,\n\tyear = {2021},\n\tnote = {\\_eprint: 2011.03459},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021', 'https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583', event);\">\n    Multi-Domain and Explainable Prediction of Changes in Web Vocabularies.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Meroño-Peñuela, A.; Pernisch, R.; Guéret, C.;  and Schlobach, S.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 11th on Knowledge Capture Conference</i>, of <i>K-CAP '21</i>, pages 193–200, New York, NY, USA, December 2021. Association for Computing Machinery\n  <span class=\"note\">event-place: Virtual Event, USA</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583\"\n     onclick=\"log_download('meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021', 'https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Multi-Domain and Explainable Prediction of Changes in Web Vocabularies [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3460210.3493583\"\n     onclick=\"log_download('meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021', 'http://doi.org/10.1145/3460210.3493583', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('meroopeuela-pernisch-guret-schlobach-multidomainandexplainablepredictionofchangesinwebvocabularies-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{merono-penuela_multi-domain_2021,\n\taddress = {New York, NY, USA},\n\tseries = {K-{CAP} &#x27;21},\n\ttitle = {Multi-{Domain} and {Explainable} {Prediction} of {Changes} in {Web} {Vocabularies}},\n\tisbn = {978-1-4503-8457-5},\n\turl = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3460210.3493583},\n\tdoi = {10.1145/3460210.3493583},\n\tabstract = {Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.},\n\tlanguage = {English},\n\tbooktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Meroño-Peñuela, Albert and Pernisch, Romana and Guéret, Christophe and Schlobach, Stefan},\n\tmonth = dec,\n\tyear = {2021},\n\tnote = {event-place: Virtual Event, USA},\n\tkeywords = {change modelling, ontology evolution, vocabulary change},\n\tpages = {193--200},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3460231.3470936\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021', 'https://dl.acm.org/doi/10.1145/3460231.3470936', event);\">\n    MORS 2021: 1st Workshop on Multi-Objective Recommender Systems.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Abdollahpouri, H.; Elahi, M.; Mansoury, M.; Sahebi, S.; Nazari, Z.; Chaney, A.;  and Loni, B.\n</span>\n\n  \n\n</span>\n\n  In <i>Fifteenth ACM Conference on Recommender Systems</i>, pages 787–788, Amsterdam Netherlands, September 2021. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3460231.3470936\"\n     onclick=\"log_download('abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021', 'https://dl.acm.org/doi/10.1145/3460231.3470936', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"MORS 2021: 1st Workshop on Multi-Objective Recommender Systems [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3460231.3470936\"\n     onclick=\"log_download('abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021', 'http://doi.org/10.1145/3460231.3470936', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('abdollahpouri-elahi-mansoury-sahebi-nazari-chaney-loni-mors20211stworkshoponmultiobjectiverecommendersystems-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{abdollahpouri_mors_2021,\n\taddress = {Amsterdam Netherlands},\n\ttitle = {{MORS} 2021: 1st {Workshop} on {Multi}-{Objective} {Recommender} {Systems}},\n\tisbn = {978-1-4503-8458-2},\n\tshorttitle = {{MORS} 2021},\n\turl = {https://dl.acm.org/doi/10.1145/3460231.3470936},\n\tdoi = {10.1145/3460231.3470936},\n\tlanguage = {en},\n\turldate = {2023-04-20},\n\tbooktitle = {Fifteenth {ACM} {Conference} on {Recommender} {Systems}},\n\tpublisher = {ACM},\n\tauthor = {Abdollahpouri, Himan and Elahi, Mehdi and Mansoury, Masoud and Sahebi, Shaghayegh and Nazari, Zahra and Chaney, Allison and Loni, Babak},\n\tmonth = sep,\n\tyear = {2021},\n\tpages = {787--788},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772', event);\">\n    Knowledge Graphs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hogan, A.; Blomqvist, E.; Cochez, M.; D’amato, C.; Melo, G. D.; Gutierrez, C.; Kirrane, S.; Gayo, J. E. L.; Navigli, R.; Neumaier, S.; Ngomo, A. N.; Polleres, A.; Rashid, S. M.; Rula, A.; Schmelzeisen, L.; Sequeda, J.; Staab, S.;  and Zimmermann, A.\n</span>\n\n  \n\n</span>\n\n  <i>ACM Comput. Surv.</i>, 54(4). July 2021.\n  <span class=\"note\">Place: New York, NY, USA Publisher: Association for Computing Machinery</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772\"\n     onclick=\"log_download('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Knowledge Graphs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3447772\"\n     onclick=\"log_download('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'http://doi.org/10.1145/3447772', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>13 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{hogan_knowledge_2021,\n\ttitle = {Knowledge {Graphs}},\n\tvolume = {54},\n\tissn = {0360-0300},\n\turl = {https://doi-org.vu-nl.idm.oclc.org/10.1145/3447772},\n\tdoi = {10.1145/3447772},\n\tabstract = {In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.},\n\tnumber = {4},\n\tjournal = {ACM Comput. Surv.},\n\tauthor = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and D’amato, Claudia and Melo, Gerard De and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and Ngomo, Axel-Cyrille Ngonga and Polleres, Axel and Rashid, Sabbir M. and Rula, Anisa and Schmelzeisen, Lukas and Sequeda, Juan and Staab, Steffen and Zimmermann, Antoine},\n\tmonth = jul,\n\tyear = {2021},\n\tnote = {Place: New York, NY, USA\nPublisher: Association for Computing Machinery},\n\tkeywords = {Knowledge graphs, embeddings, graph algorithms, graph databases, graph neural networks, graph query languages, ontologies, rule mining, shapes},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://kgbook.org\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'https://kgbook.org', event);\">\n    Knowledge graphs.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Hogan, A.; Blomqvist, E.; Cochez, M.; d'Amato , C.; Melo, G. d.; Gutierrez, C.; Kirrane, S.; Gayo, J. E. L.; Navigli, R.; Neumaier, S.;  and others\n</span>\n\n  \n\n</span>\n\n  Volume 12 of Synthesis Lectures on Data, Semantics, and KnowledgeSpringer (formerly Morgan & Claypool Publishers),  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://kgbook.org\"\n     onclick=\"log_download('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'https://kgbook.org', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Knowledge graphs [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.2200/S01125ED1V01Y202109DSK022\"\n     onclick=\"log_download('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021', 'http://doi.org/10.2200/S01125ED1V01Y202109DSK022', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>13 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('hogan-blomqvist-cochez-damato-melo-gutierrez-kirrane-gayo-etal-knowledgegraphs-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@book{hogan_knowledge_2021-1,\n\tseries = {Synthesis {Lectures} on {Data}, {Semantics}, and {Knowledge}},\n\ttitle = {Knowledge graphs},\n\tvolume = {12},\n\tisbn = {978-3-031-01918-0},\n\turl = {https://kgbook.org},\n\tpublisher = {Springer (formerly Morgan \\&amp; Claypool Publishers)},\n\tauthor = {Hogan, Aidan and Blomqvist, Eva and Cochez, Michael and d&#x27;Amato, Claudia and Melo, Gerard de and Gutierrez, Claudio and Kirrane, Sabrina and Gayo, José Emilio Labra and Navigli, Roberto and Neumaier, Sebastian and {others}},\n\tyear = {2021},\n\tdoi = {10.2200/S01125ED1V01Y202109DSK022},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3460210.3493540\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021', 'https://dl.acm.org/doi/10.1145/3460210.3493540', event);\">\n    Toward Measuring the Resemblance of Embedding Models for Evolving Ontologies.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pernisch, R.; Dell'Aglio, D.;  and Bernstein, A.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 11th on Knowledge Capture Conference</i>, pages 177–184, Virtual Event USA, December 2021. ACM\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://dl.acm.org/doi/10.1145/3460210.3493540\"\n     onclick=\"log_download('pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021', 'https://dl.acm.org/doi/10.1145/3460210.3493540', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Toward Measuring the Resemblance of Embedding Models for Evolving Ontologies [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3460210.3493540\"\n     onclick=\"log_download('pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021', 'http://doi.org/10.1145/3460210.3493540', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pernisch-dellaglio-bernstein-towardmeasuringtheresemblanceofembeddingmodelsforevolvingontologies-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pernisch_toward_2021,\n\taddress = {Virtual Event USA},\n\ttitle = {Toward {Measuring} the {Resemblance} of {Embedding} {Models} for {Evolving} {Ontologies}},\n\tisbn = {978-1-4503-8457-5},\n\turl = {https://dl.acm.org/doi/10.1145/3460210.3493540},\n\tdoi = {10.1145/3460210.3493540},\n\tlanguage = {en},\n\turldate = {2021-12-08},\n\tbooktitle = {Proceedings of the 11th on {Knowledge} {Capture} {Conference}},\n\tpublisher = {ACM},\n\tauthor = {Pernisch, Romana and Dell&#x27;Aglio, Daniele and Bernstein, Abraham},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {177--184},\n}\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (6)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2004.04582\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020', 'https://arxiv.org/abs/2004.04582', event);\">\n    DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karim, M. R.; Döhmen, T.; Cochez, M.; Beyan, O.; Rebholz-Schuhmann, D.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  In <i>2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</i>, pages 1034–1037,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2004.04582\"\n     onclick=\"log_download('karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020', 'https://arxiv.org/abs/2004.04582', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/BIBM49941.2020.9313304\"\n     onclick=\"log_download('karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020', 'http://doi.org/10.1109/BIBM49941.2020.9313304', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karim-dhmen-cochez-beyan-rebholzschuhmann-decker-deepcovidexplainerexplainablecovid19diagnosisfromchestxrayimages-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{karim_deepcovidexplainer_2020,\n\ttitle = {{DeepCOVIDExplainer}: {Explainable} {COVID}-19 {Diagnosis} from {Chest} {X}-ray {Images}},\n\turl = {https://arxiv.org/abs/2004.04582},\n\tdoi = {10.1109/BIBM49941.2020.9313304},\n\tbooktitle = {2020 {IEEE} {International} {Conference} on {Bioinformatics} and {Biomedicine} ({BIBM})},\n\tauthor = {Karim, Md. Rezaul and Döhmen, Till and Cochez, Michael and Beyan, Oya and Rebholz-Schuhmann, Dietrich and Decker, Stefan},\n\tyear = {2020},\n\tpages = {1034--1037},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2004.07807\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020', 'https://arxiv.org/abs/2004.07807', event);\">\n    Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutifonal-LSTM Network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karim, M. R.; Raja Chakravarthi, B.; McCrae, J. P.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In <i>2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)</i>, pages 390–399,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2004.07807\"\n     onclick=\"log_download('karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020', 'https://arxiv.org/abs/2004.07807', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutifonal-LSTM Network [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/DSAA49011.2020.00053\"\n     onclick=\"log_download('karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020', 'http://doi.org/10.1109/DSAA49011.2020.00053', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karim-rajachakravarthi-mccrae-cochez-classificationbenchmarksforunderresourcedbengalilanguagebasedonmultichannelconvolutifonallstmnetwork-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{karim_classification_2020,\n\ttitle = {Classification {Benchmarks} for {Under}-resourced {Bengali} {Language} based on {Multichannel} {Convolutifonal}-{LSTM} {Network}},\n\turl = {https://arxiv.org/abs/2004.07807},\n\tdoi = {10.1109/DSAA49011.2020.00053},\n\tbooktitle = {2020 {IEEE} 7th {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})},\n\tauthor = {Karim, Md. Rezaul and Raja Chakravarthi, Bharathi and McCrae, John P. and Cochez, Michael},\n\tyear = {2020},\n\tpages = {390--399},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pellegrino-altabba-garofalo-ristoski-cochez-gevalamodularandextensibleevaluationframeworkforgraphembeddingtechniques-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pellegrino, M. A.; Altabba, A.; Garofalo, M.; Ristoski, P.;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In Harth, A.; Kirrane, S.; Ngonga Ngomo, A.; Paulheim, H.; Rula, A.; Gentile, A. L.; Haase, P.;  and Cochez, M., editor(s), <i>The Semantic Web</i>, pages 565–582, Cham,  2020. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pellegrino-altabba-garofalo-ristoski-cochez-gevalamodularandextensibleevaluationframeworkforgraphembeddingtechniques-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pellegrino-altabba-garofalo-ristoski-cochez-gevalamodularandextensibleevaluationframeworkforgraphembeddingtechniques-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{pellegrino_geval_2020,\n\taddress = {Cham},\n\ttitle = {{GEval}: {A} {Modular} and {Extensible} {Evaluation} {Framework} for {Graph} {Embedding} {Techniques}},\n\tisbn = {978-3-030-49461-2},\n\tabstract = {While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.},\n\tbooktitle = {The {Semantic} {Web}},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Pellegrino, Maria Angela and Altabba, Abdulrahman and Garofalo, Martina and Ristoski, Petar and Cochez, Michael},\n\teditor = {Harth, Andreas and Kirrane, Sabrina and Ngonga Ngomo, Axel-Cyrille and Paulheim, Heiko and Rula, Anisa and Gentile, Anna Lisa and Haase, Peter and Cochez, Michael},\n\tyear = {2020},\n\tpages = {565--582},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1093/bib/bbz170\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020', 'https://doi.org/10.1093/bib/bbz170', event);\">\n    Deep learning-based clustering approaches for bioinformatics.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Karim, M. R.; Beyan, O.; Zappa, A.; Costa, I. G; Rebholz-Schuhmann, D.; Cochez, M.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  <i>Briefings in Bioinformatics</i>. February 2020.\n  <span class=\"note\">_eprint: https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbz170/32313172/bbz170.pdf</span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1093/bib/bbz170\"\n     onclick=\"log_download('karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020', 'https://doi.org/10.1093/bib/bbz170', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Deep learning-based clustering approaches for bioinformatics [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1093/bib/bbz170\"\n     onclick=\"log_download('karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020', 'http://doi.org/10.1093/bib/bbz170', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('karim-beyan-zappa-costa-rebholzschuhmann-cochez-decker-deeplearningbasedclusteringapproachesforbioinformatics-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{karim_deep_2020,\n\ttitle = {Deep learning-based clustering approaches for bioinformatics},\n\tissn = {1477-4054},\n\turl = {https://doi.org/10.1093/bib/bbz170},\n\tdoi = {10.1093/bib/bbz170},\n\tabstract = {Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.},\n\tjournal = {Briefings in Bioinformatics},\n\tauthor = {Karim, Md Rezaul and Beyan, Oya and Zappa, Achille and Costa, Ivan G and Rebholz-Schuhmann, Dietrich and Cochez, Michael and Decker, Stefan},\n\tmonth = feb,\n\tyear = {2020},\n\tnote = {\\_eprint: https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbz170/32313172/bbz170.pdf},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Clustering is central to many data-driven bioinformatics research and serves a powerful computational method. In particular, clustering helps at analyzing unstructured and high-dimensional data in the form of sequences, expressions, texts and images. Further, clustering is used to gain insights into biological processes in the genomics level, e.g. clustering of gene expressions provides insights on the natural structure inherent in the data, understanding gene functions, cellular processes, subtypes of cells and understanding gene regulations. Subsequently, clustering approaches, including hierarchical, centroid-based, distribution-based, density-based and self-organizing maps, have long been studied and used in classical machine learning settings. In contrast, deep learning (DL)-based representation and feature learning for clustering have not been reviewed and employed extensively. Since the quality of clustering is not only dependent on the distribution of data points but also on the learned representation, deep neural networks can be effective means to transform mappings from a high-dimensional data space into a lower-dimensional feature space, leading to improved clustering results. In this paper, we review state-of-the-art DL-based approaches for cluster analysis that are based on representation learning, which we hope to be useful, particularly for bioinformatics research. Further, we explore in detail the training procedures of DL-based clustering algorithms, point out different clustering quality metrics and evaluate several DL-based approaches on three bioinformatics use cases, including bioimaging, cancer genomics and biomedical text mining. We believe this review and the evaluation results will provide valuable insights and serve a starting point for researchers wanting to apply DL-based unsupervised methods to solve emerging bioinformatics research problems.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://doi.org/10.1007/s10115-019-01401-x\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020', 'https://doi.org/10.1007/s10115-019-01401-x', event);\">\n    Structured query construction via knowledge graph embedding.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang, R.; Wang, M.; Liu, J.; Cochez, M.;  and Decker, S.\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge and Information Systems</i>, 62(5): 1819–1846. May 2020.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://doi.org/10.1007/s10115-019-01401-x\"\n     onclick=\"log_download('wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020', 'https://doi.org/10.1007/s10115-019-01401-x', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Structured query construction via knowledge graph embedding [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10115-019-01401-x\"\n     onclick=\"log_download('wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020', 'http://doi.org/10.1007/s10115-019-01401-x', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-wang-liu-cochez-decker-structuredqueryconstructionviaknowledgegraphembedding-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{wang_structured_2020,\n\ttitle = {Structured query construction via knowledge graph embedding},\n\tvolume = {62},\n\tissn = {0219-3116},\n\turl = {https://doi.org/10.1007/s10115-019-01401-x},\n\tdoi = {10.1007/s10115-019-01401-x},\n\tabstract = {In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.},\n\tnumber = {5},\n\tjournal = {Knowledge and Information Systems},\n\tauthor = {Wang, Ruijie and Wang, Meng and Liu, Jun and Cochez, Michael and Decker, Stefan},\n\tmonth = may,\n\tyear = {2020},\n\tpages = {1819--1846},\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"daza-cochez-messagepassingqueryembedding-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://arxiv.org/abs/2002.02406\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'daza-cochez-messagepassingqueryembedding-2020', 'https://arxiv.org/abs/2002.02406', event);\">\n    Message Passing Query Embedding.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://discoverylab.ai/\">Daza, D.</a>;  and Cochez, M.\n</span>\n\n  \n\n</span>\n\n  In <i>ICML Workshop - Graph Representation Learning and Beyond</i>,  2020. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://arxiv.org/abs/2002.02406\"\n     onclick=\"log_download('daza-cochez-messagepassingqueryembedding-2020', 'https://arxiv.org/abs/2002.02406', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Message Passing Query Embedding [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/daza-cochez-messagepassingqueryembedding-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('daza-cochez-messagepassingqueryembedding-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{daza_message_2020,\n\ttitle = {Message {Passing} {Query} {Embedding}},\n\turl = {https://arxiv.org/abs/2002.02406},\n\tbooktitle = {{ICML} {Workshop} - {Graph} {Representation} {Learning} and {Beyond}},\n\tauthor = {Daza, Daniel and Cochez, Michael},\n\tyear = {2020},\n}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);