Abstract
We propose a model which jointly learns the embeddings of multiple knowledge bases (KBs) in a uniform vector space to align entities in KBs. Instead of using content similarity based methods, we think the structure information of KBs is also important for KB alignment. When facing the cross-linguistic or different encoding situation, what we can leverage are only the structure information of two KBs. We utilize seed entity alignments whose embeddings are ensured the same in the joint learning process. We perform experiments on two datasets including a subset of Freebase comprising 15 thousand selected entities, and a dataset we construct from real-world large scale KBs – Freebase and DBpedia. The results show that the proposed approach which only utilize the structure information of KBs also works well.
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Notes
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In step5, 7 and 35 are empirical values chosen in experiments.
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Acknowledgement
This work was supported by the Natural Science Foundation of China (No. 61533018), the National Basic Research Program of China (No. 2014CB340503) and the National Natural Science Foundation of China (No. 61272332). And this work was also supported by Google through focused research awards program.
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Hao, Y., Zhang, Y., He, S., Liu, K., Zhao, J. (2016). A Joint Embedding Method for Entity Alignment of Knowledge Bases. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_1
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