Dangling-Aware Entity Alignment with Mixed High-Order Proximities
This addresses a more realistic scenario in entity alignment for knowledge graph integration, though it appears incremental by extending prior work to handle dangling entities.
The paper tackles the problem of dangling-aware entity alignment in knowledge graphs, where some entities lack counterparts in other KGs, and proposes a framework using mixed high-order proximities that more precisely detects dangling entities and better aligns matchable entities.
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.