Dangling-Aware Entity Alignment with Mixed High-Order Proximities

TL;DR

This paper introduces a framework for entity alignment in knowledge graphs that accounts for dangling entities, leveraging mixed high-order proximities to improve detection and alignment accuracy.

Contribution

It proposes a novel approach combining local and global high-order proximities for dangling-aware entity alignment, addressing a gap in existing methods.

Findings

More accurate dangling entity detection

Improved entity alignment performance

Mitigates hubness problem in KGs

Abstract

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 framework 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.