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We study dangling-aware entity alignment in knowledge graphs (KGs), which isan underexplored but important problem. As different KGs are naturallyconstructed by different sets of entities, a KG commonly contains some danglingentities that cannot find counterparts in other KGs. Therefore, dangling-awareentity alignment is more realistic than the conventional entity alignment whereprior studies simply ignore dangling entities. We propose a framework usingmixed high-order proximities on dangling-aware entity alignment. Our frameworkutilizes both the local high-order proximity in a nearest neighbor subgraph andthe global high-order proximity in an embedding space for both danglingdetection and entity alignment. Extensive experiments with two evaluationsettings shows that our framework more precisely detects dangling entities, andbetter aligns matchable entities. Further investigations demonstrate that ourframework can mitigate the hubness problem on dangling-aware entity alignment.

Dangling-Aware Entity Alignment with Mixed High-Order Proximities