Qualifier Recommendation for Wikidata. Ducu, A. M. & Cochez, M. In Athens, Greece, November, 2023. ![Qualifier Recommendation for Wikidata [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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.
@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},
}
Downloads: 0
{"_id":"NbYsvHaqgZc2wjiFb","bibbaseid":"ducu-cochez-qualifierrecommendationforwikidata-2023","author_short":["Ducu, A. M.","Cochez, M."],"bibdata":{"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":{}}},"bibtype":"inproceedings","biburl":"https://api.zotero.org/groups/4799514/items?key=euYq5cdKpGK6xljQmpW8AeSb&format=bibtex&limit=100","dataSources":["qyx6bB8ujfH9zqTzu","jRiMQcLQ4TWHSS6Mc"],"keywords":[],"search_terms":["qualifier","recommendation","wikidata","ducu","cochez"],"title":"Qualifier Recommendation for Wikidata","year":2023}