ARQMath Lab: An Incubator for Semantic Formula Search in zbMATH Open?. Scharpf, P., Schubotz, M., Greiner-Petter, A., Ostendorff, M., Teschke, O., & Gipp, B. In Working Notes of (CLEF) 2020 - Conference and Labs of the Evaluation Forum, volume 2696, Thessaloniki, Greece, 2020. CEUR-WS.org. ![ARQMath Lab: An Incubator for Semantic Formula Search in zbMATH Open? [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 1 download The zbMATH database contains more than 4 million bibliographic entries. We aim to provide easy access to these entries. Therefore, we maintain dif-ferent index structures including a formula index. To optimize the findability of the entries in our database, we constantly investigate new approaches to satisfy the information needs of our users. We believe that the findings from the ARQMath evaluation will generate new insights into which index struc-tures are most suitable to satisfy mathematical information needs. Search en-gines, recommender systems, plagiarism checking software, and many other added-value services acting on databases such as the arXiv and zbMATH need to combine natural and formula language. One initial approach to ad-dress this challenge is to enrich the mostly unstructured document data via Entity Linking. The ARQMath Task at CLEF 2020 aims to tackle the problem of linking newly posted questions from Math Stack Exchange (MSE) to exist-ing ones that were already answered by the community. To deeply under-stand MSE information needs, answer-, and formula types, we performed manual runs for tasks 1 and 2. Furthermore, we explored several formula re-trieval methods for task 2, such as fuzzy string search, k-nearest neighbors, and our recently introduced approach to retrieve Mathematical Objects of In-terest (MOI) with textual search queries. The task results show that neither our automated methods nor our manual runs archived good scores in the competition. However, the perceived quality of the hits returned by the MOI search particularly motivates us to conduct further research about MOI.
@inproceedings{BibbaseScharpfSGOTG20,
address = {Thessaloniki, Greece},
title = {{ARQMath} {Lab}: {An} {Incubator} for {Semantic} {Formula} {Search} in {zbMATH} {Open}?},
volume = {2696},
url = {http://ceur-ws.org/Vol-2696/paper_200.pdf},
abstract = {The zbMATH database contains more than 4 million bibliographic entries. We aim to provide easy access to these entries. Therefore, we maintain dif-ferent index structures including a formula index. To optimize the findability of the entries in our database, we constantly investigate new approaches to satisfy the information needs of our users. We believe that the findings from the ARQMath evaluation will generate new insights into which index struc-tures are most suitable to satisfy mathematical information needs. Search en-gines, recommender systems, plagiarism checking software, and many other added-value services acting on databases such as the arXiv and zbMATH need to combine natural and formula language. One initial approach to ad-dress this challenge is to enrich the mostly unstructured document data via Entity Linking. The ARQMath Task at CLEF 2020 aims to tackle the problem of linking newly posted questions from Math Stack Exchange (MSE) to exist-ing ones that were already answered by the community. To deeply under-stand MSE information needs, answer-, and formula types, we performed manual runs for tasks 1 and 2. Furthermore, we explored several formula re-trieval methods for task 2, such as fuzzy string search, k-nearest neighbors, and our recently introduced approach to retrieve Mathematical Objects of In-terest (MOI) with textual search queries. The task results show that neither our automated methods nor our manual runs archived good scores in the competition. However, the perceived quality of the hits returned by the MOI search particularly motivates us to conduct further research about MOI.},
booktitle = {Working {Notes} of ({CLEF}) 2020 - {Conference} and {Labs} of the {Evaluation} {Forum}},
publisher = {CEUR-WS.org},
author = {Scharpf, Philipp and Schubotz, Moritz and Greiner-Petter, Andre and Ostendorff, Malte and Teschke, Olaf and Gipp, Bela},
year = {2020},
}
Downloads: 1
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We believe that the findings from the ARQMath evaluation will generate new insights into which index struc-tures are most suitable to satisfy mathematical information needs. Search en-gines, recommender systems, plagiarism checking software, and many other added-value services acting on databases such as the arXiv and zbMATH need to combine natural and formula language. One initial approach to ad-dress this challenge is to enrich the mostly unstructured document data via Entity Linking. The ARQMath Task at CLEF 2020 aims to tackle the problem of linking newly posted questions from Math Stack Exchange (MSE) to exist-ing ones that were already answered by the community. To deeply under-stand MSE information needs, answer-, and formula types, we performed manual runs for tasks 1 and 2. Furthermore, we explored several formula re-trieval methods for task 2, such as fuzzy string search, k-nearest neighbors, and our recently introduced approach to retrieve Mathematical Objects of In-terest (MOI) with textual search queries. The task results show that neither our automated methods nor our manual runs archived good scores in the competition. 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