Abstract
We study the problem of structure-based entity alignment between knowledge graphs (KGs). The recent mainstream solutions for it apply KG embedding techniques to map entities into a vector space, where the similarity between entities could be measured accordingly. However, these methods which are mostly based on TransE and its variants treat relation triples in KGs independently. As a result, they fail to capture some advanced interactions between entities that are implicit in the surrounding and multi-hop entities: One is the differences between the one-hop and two-hop neighborhood of an entity, which we call as short-term differences, while the other is the dependencies between entities that are far apart, which we call as long-term dependencies. Based on the above observations, this paper proposes a novel approach learning to capture both the short-term differences and the long-term dependencies in KGs for entity alignment using graph neural networks and self-attention mechanisms respectively. Our empirical study conducted on four couples of real-world datasets shows the superiority of our model, compared with the state-of-the-art methods.
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Acknowledgments
This research is partially supported by National Key R&D Program of China (No. 2018AAA0101900), the Priority Academic Program Development of Jiangsu Higher Education Institutions, Natural Science Foundation of Jiangsu Province (No. BK20191420), National Natural Science Foundation of China (Grant No. 62072323, 61632016), Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010).
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Chen, J. et al. (2020). Learning Short-Term Differences and Long-Term Dependencies for Entity Alignment. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_6
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DOI: https://doi.org/10.1007/978-3-030-62419-4_6
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