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Title: Dangling-Aware Entity Alignment with Mixed High-Order Proximities
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.  more » « less
Award ID(s):
2105329
PAR ID:
10343364
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Findings of the Association for Computational Linguistics: NAACL 2022
Page Range / eLocation ID:
1172 to 1184
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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