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Publication type: Conference paper
Type of review: Peer review (publication)
Title: VersaMatch : ontology matching with weak supervision
Authors: Fürst, Jonathan
Fadel Argerich, Mauricio
Cheng, Bin
et. al: No
DOI: 10.14778/3583140.3583148
Proceedings: Proceedings of the VLDB Endowment
Editors of the parent work: Koutrika, Georgia
Yang, Jun
Volume(Issue): 16
Issue: 6
Page(s): 1305
Pages to: 1318
Conference details: 49th Conference on Very Large Data Bases (VLDB), Vancouver, Canada, 28 August - 1 September 2023
Issue Date: 2023
Publisher / Ed. Institution: Association for Computing Machinery
ISSN: 2150-8097
Language: English
Subjects: Ontology matching; Weak supervision; Machine learning; Data integration
Subject (DDC): 006: Special computer methods
Abstract: Ontology matching is crucial to data integration for across-silo data sharing and has been mainly addressed with heuristic and machine learning (ML) methods. While heuristic methods are often inflexible and hard to extend to new domains, ML methods rely on substantial and hard to obtain amounts of labeled training data. To overcome these limitations, we propose VersaMatch, a flexible, weakly-supervised ontology matching system. VersaMatch employs various weak supervision sources, such as heuristic rules, pattern matching, and external knowledge bases, to produce labels from a large amount of unlabeled data for training a discriminative ML model. For prediction, VersaMatch develops a novel ensemble model combining the weak supervision sources with the discriminative model to support generalization while retaining a high precision. Our ensemble method boosts end model performance by 4 points compared to a traditional weak-supervision baseline. In addition, compared to state-of-the-art ontology matchers, VersaMatch achieves an overall 4-point performance improvement in F1 score across 26 ontology combinations from different domains. For recently released, in-the-wild datasets, VersaMatch beats the next best matchers by 9 points in F1. Furthermore, its core weak-supervision logic can easily be improved by adding more knowledge sources and collecting more unlabeled data for training.
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Fulltext version: Published version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Computer Science (InIT)
Appears in collections:Publikationen School of Engineering

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Fürst, J., Fadel Argerich, M., & Cheng, B. (2023). VersaMatch : ontology matching with weak supervision [Conference paper]. In G. Koutrika & J. Yang (Eds.), Proceedings of the VLDB Endowment (Vol. 16, Issue 6, pp. 1305–1318). Association for Computing Machinery.
Fürst, J., Fadel Argerich, M. and Cheng, B. (2023) ‘VersaMatch : ontology matching with weak supervision’, in G. Koutrika and J. Yang (eds) Proceedings of the VLDB Endowment. Association for Computing Machinery, pp. 1305–1318. Available at:
J. Fürst, M. Fadel Argerich, and B. Cheng, “VersaMatch : ontology matching with weak supervision,” in Proceedings of the VLDB Endowment, 2023, vol. 16, no. 6, pp. 1305–1318. doi: 10.14778/3583140.3583148.
FÜRST, Jonathan, Mauricio FADEL ARGERICH und Bin CHENG, 2023. VersaMatch : ontology matching with weak supervision. In: Georgia KOUTRIKA und Jun YANG (Hrsg.), Proceedings of the VLDB Endowment. Conference paper. Association for Computing Machinery. 2023. S. 1305–1318
Fürst, Jonathan, Mauricio Fadel Argerich, and Bin Cheng. 2023. “VersaMatch : Ontology Matching with Weak Supervision.” Conference paper. In Proceedings of the VLDB Endowment, edited by Georgia Koutrika and Jun Yang, 16:1305–18. Association for Computing Machinery.
Fürst, Jonathan, et al. “VersaMatch : Ontology Matching with Weak Supervision.” Proceedings of the VLDB Endowment, edited by Georgia Koutrika and Jun Yang, vol. 16, no. 6, Association for Computing Machinery, 2023, pp. 1305–18,

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