Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-27771Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Fürst, Jonathan | - |
| dc.contributor.author | Fadel Argerich, Mauricio | - |
| dc.contributor.author | Cheng, Bin | - |
| dc.date.accessioned | 2023-04-28T13:30:38Z | - |
| dc.date.available | 2023-04-28T13:30:38Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.issn | 2150-8097 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/27771 | - |
| dc.description.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. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Association for Computing Machinery | de_CH |
| dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | de_CH |
| dc.subject | Ontology matching | de_CH |
| dc.subject | Weak supervision | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject | Data integration | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.title | VersaMatch : ontology matching with weak supervision | de_CH |
| dc.type | Konferenz: Paper | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Informatik (InIT) | de_CH |
| dc.identifier.doi | 10.14778/3583140.3583148 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-27771 | - |
| zhaw.conference.details | 49th Conference on Very Large Data Bases (VLDB), Vancouver, Canada, 28 August - 1 September 2023 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 6 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 1318 | de_CH |
| zhaw.pages.start | 1305 | de_CH |
| zhaw.parentwork.editor | Koutrika, Georgia | - |
| zhaw.parentwork.editor | Yang, Jun | - |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 16 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.title.proceedings | Proceedings of the VLDB Endowment | de_CH |
| zhaw.webfeed | Intelligent Information Systems | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| zhaw.relation.references | https://github.com/nec-research/VersaMatch | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Fuerst-etal_VersaMatch-ontology-matching-with-weak-supervision.pdf | 781.26 kB | Adobe PDF |  View/Open |
Show simple item record
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. https://doi.org/10.14778/3583140.3583148
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: https://doi.org/10.14778/3583140.3583148.
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. https://doi.org/10.14778/3583140.3583148.
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, https://doi.org/10.14778/3583140.3583148.
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