Wu, Y, Liu, X, Feng, Y et al. (2 more authors) (2020) Neighborhood Matching Network for Entity Alignment. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. The 58th Annual Meeting of the Association for Computational Linguistics (ACL), 05-10 Jul 2020, Online. Association for Computational Linguistics , pp. 6477-6487. ISBN 978-1-950737-48-2
Abstract
Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Association for Computational Linguistics. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 May 2020 13:30 |
Last Modified: | 07 Aug 2023 08:26 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Identification Number: | 10.18653/v1/2020.acl-main.578 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160214 |