Exploiting Transitivity for Entity Matching
Publication date
2021-07-31
Authors
Baas, J.
Dastani, Mehdi
Feelders, Ad
Editors
Verborgh, Ruben
Dimou, Anastasia
Hogan, Aidan
d'Amato, Claudia
Tiddi, Ilaria
Bröring, Arne
Mayer, Simon
Ongenae, Femke
Tommasini, Riccardo
Alam, Mehwish
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
taverne
Abstract
The goal of entity matching in knowledge graphs is to identify sets of entities that refer to the same real-world object. Methods for entity matching in knowledge graphs, however, produce a collection of pairs of entities claimed to be duplicates. This collection that represents the sameAs relation may fail to satisfy some of its structural properties such as transitivity. We show that an ad-hoc enforcement of transitivity on the set of identified entity pairs may decrease precision. We therefore propose a methodology that starts with a given similarity measure, generates a set of entity pairs, and applies cluster editing to enforce transitivity, leading to overall improved performance.
Keywords
Entity Matching, Digital Humantities, Taverne
Citation
Baas, J, Dastani, M & Feelders, A 2021, Exploiting Transitivity for Entity Matching. in R Verborgh, A Dimou, A Hogan, C d'Amato, I Tiddi, A Bröring, S Mayer, F Ongenae, R Tommasini & M Alam (eds), The Semantic Web: ESWC 2021 Satellite Events : Virtual Event, June 6–10, 2021, Revised Selected Papers. Lecture Notes in Computer Science, vol. 12739, Springer, pp. 109-114, European Semantic Web Conference, Hersonissos, Greece, 6/06/21. https://doi.org/10.1007/978-3-030-80418-3_20, conference