A semi-supervised framework fusing multiple information for knowledge graph entity alignment

计算机科学 知识图 图形 人工智能 情报检索 数据挖掘 理论计算机科学
作者
Zepeng Li,Nengneng Ding,Chenhui Liang,Shuo Cao,Minyu Zhai,Rikui Huang,Zhenwen Zhang,Bin Hu
出处
期刊:Expert Systems With Applications [Elsevier]
卷期号:259: 125282-125282
标识
DOI:10.1016/j.eswa.2024.125282
摘要

Entity alignment (EA) is a fundamental task for cross linguistic knowledge graphs (KGs) understanding and interaction, which is committed to matching entities from different graphs based on their inherent semantics. Methods based on graph neural networks (GNNs) dominate the EA task, however, the majority of them ignore the higher-order information among entities in the KGs. Meanwhile, as important auxiliary information, the relational semantics, string information of entity names and attribute information of entities are insufficiently exploited during the inference phase. In addition, labeled alignment data is universally insufficient across various datasets, which limits the performance of the model. In this paper, we propose a Semi-supervised EA framework that Comprehensively considers both Structural and Attribute information within KGs (SCSA) to address these problems above. Specifically, our approach first leverages hypergraph neural networks (HGNN) to aggregate relational semantic information and graph convolutional networks (GCNs) with a highway filtering strategy to acquire the embedding representation of entities precisely. Then, we propose a bidirectional filtering technique with a combination of entity, attribute and string values to create pseudo-labeled data and lead the model for iteratively training. We implement our proposed framework on several publicly recognized cross-lingual datasets. The experimental results indicate that our framework outperforms almost all state-of-the-art (SOTA) methods.
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