Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation

• URL: http://arxiv.org/abs/2402.03025v2
• Date: Sat, 12 Oct 2024 16:28:32 GMT
• Title: Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
• Authors: Yuanyi Wang, Wei Tang, Haifeng Sun, Zirui Zhuang, Xiaoyuan Fu, Jingyu Wang, Qi Qi, Jianxin Liao,
• Abstract summary: We present a propagation perspective to analyze weakly supervised EA. We show that aggregation-based EA models seek propagation operators for pairwise entity similarities. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model.
• Score: 31.558938631213074
• License:
• Abstract: Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models. Our theoretical analysis reveals that these models essentially seek propagation operators for pairwise entity similarities. We further prove that, despite the structural heterogeneity of different KGs, the potentially aligned entities within aggregation-based EA models have isomorphic subgraphs, which is the core premise of EA but has not been investigated. Leveraging this insight, we introduce a potential isomorphism propagation operator to enhance the propagation of neighborhood information across KGs. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model without altering the learning process. Extensive experiments substantiate our theoretical findings and demonstrate PipEA's significant performance gains over state-of-the-art weakly supervised EA methods. Our work not only advances the field but also enhances our comprehension of aggregation-based weakly supervised EA.

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