Abstract
The knowledge representation framework of MDATA requires the fusion of multi-source and multi-dimensional data. The main steps of data fusion are entity alignment and disambiguation. The method of entity alignment mainly includes the similarity calculation of entity description text and entity embedding. The embedding-based entity alignment method usually uses pre-aligned entities as seed data, and aligns the entities in different knowledge graphs through seed entity constraints. This method relies heavily on the quality and quantity of seed entities. In this chapter, we introduce an algorithm to optimize the selection of seed entities, and select seed entity pairs through the centrality and differentiability of entities in the knowledge graph. In order to solve the problem of insufficient number of high-quality seed entities, an iterative entity alignment method is adopted. We have done experiments on DBP15K dataset, and the experimental results show that the proposed method can achieve good entity alignment even under weak supervision.
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Chen, X., Wang, L., Tang, Y., Han, W., Tian, Z., Gu, Z. (2021). Entity Alignment: Optimization by Seed Selection. In: Jia, Y., Gu, Z., Li, A. (eds) MDATA: A New Knowledge Representation Model. Lecture Notes in Computer Science(), vol 12647. Springer, Cham. https://doi.org/10.1007/978-3-030-71590-8_6
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DOI: https://doi.org/10.1007/978-3-030-71590-8_6
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