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.
Code (0)
등록된 구현이 없습니다.
Tasks
Entity AlignmentKnowledge GraphsVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
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 d…
Entity AlignmentKnowledge GraphsVocal Bursts Intensity PredictionKnowing the No-match: Entity Alignment with Dangling Cases
This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the proble…
Abstention PredictionEntity AlignmentKnowledge GraphsSemi-constraint Optimal Transport for Entity Alignment with Dangling Cases
Entity alignment (EA) merges knowledge graphs (KGs) by identifying the equivalent entities in different graphs, which can effectively enrich knowledge representations of KGs. However, in practice, different KGs often inc…
Entity AlignmentKnowledge GraphsMetric LearningWord EmbeddingsLambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address …
Contrastive LearningEntity AlignmentRepresentation LearningAn Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and m…
Entity AlignmentKnowledge Graphs