陈俊帆
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陈俊帆
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论文
Semi-Supervised Entity Alignment With Global Alignment and Local Information Aggregation
发布时间：2025-10-22点击次数：
发表刊物：
IEEE Transactions on Knowledge and Data Engineering (TKDE), CCF-A
摘要：
Entity alignment is a vital task in knowledge fusion, which aims to align entities from different knowledge graphs and merge them into one single graph. Existing entity alignment models focus on local features and try to minimize the distance between pairs of pre-aligned entities. Despite their success, these models heavily rely on the number of existing pre-aligned entity pairs and the topology information from the rest of the large set of unaligned entities is still largely unexplored. To overcome the limitation of existing models, we propose a model, termed Global Alignment and Local Information Aggregation, or GALA. GALA constructs global features for the knowledge graphs to be aligned using entity embeddings. It aligns the entities in the graphs by forcing their global features to match with each other and progressively updating the entity embeddings by aggregating local information from the other network. Empirical studies on commonly-used KG alignment data sets confirm the effectiveness of the proposed model.
合写作者：
Xuefeng Zhang,张日崇,陈俊帆, Jaein Kim, Yongyi Mao
论文类型：
国际刊物
页面范围：
10464-10477
是否译文：
否
发表时间：
2023-01-01