Abstract
Entity alignment aims to automatically determine whether an entity pair in different knowledge graphs refers to the same entity in reality. Existing entity alignment methods can be classified into two categories: string-similarity-based methods and embedding-based methods. String-similarity-based methods have higher accuracy, however, they might have difficulty in dealing with literal heterogeneity, i.e., an entity pair in diverse forms. Though embedding-based entity alignment can deal with literal heterogeneity, they also suffer the shortcomings of higher time complexity and lower accuracy. Moreover, there remain limitations and challenges due to only using the structure information of triples for existing embedding methods. Therefore, in this study, we propose a two-stage entity alignment framework, which can combine the advantages of both methods. In addition, to enhance the embedding performance, a hybrid knowledge graph embedding model with both fact triples and logical rules is introduced for entity alignment. Experimental results on two real-world datasets show that the proposed method is significantly better than the state-of-the-art embedding-based entity alignment methods.
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Acknowledgments
This work was partly supported by the National Key Research and Development Program of China, under grant 2016YFB1000901 and the National Natural Science Foundation of China under grant 91746209. Chenyang Bu was also partly supported by the Fundamental Research Funds for the Central Universities (No. JZ2018HGBH0279), the National Natural Science Foundation of China (No. 61573327), and the Project funded by the China Postdoctoral Science Foundation (No. 2018M630704).
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Jiang, T., Bu, C., Zhu, Y., Wu, X. (2019). Two-Stage Entity Alignment: Combining Hybrid Knowledge Graph Embedding with Similarity-Based Relation Alignment. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_13
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