Neighborhood Matching Network for Entity Alignment

Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Dongyan Zhao

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.
Anthology ID:
2020.acl-main.578
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6477–6487
Language:
URL:
https://aclanthology.org/2020.acl-main.578
DOI:
10.18653/v1/2020.acl-main.578
Bibkey:
Cite (ACL):
Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, and Dongyan Zhao. 2020. Neighborhood Matching Network for Entity Alignment. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6477–6487, Online. Association for Computational Linguistics.
Cite (Informal):
Neighborhood Matching Network for Entity Alignment (Wu et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.578.pdf
Video:
 http://slideslive.com/38929380
Code
 StephanieWyt/NMN
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