Abstract
Knowledge graph is the cornerstone of artificial intelligence. Entity alignment in multi-source online encyclopedias is an important part of data integration to construct the knowledge graph. In order to solve the problem that traditional methods are not effective enough for entity alignment in online encyclopedias tasks, this paper proposes the Chinese Online Encyclopedia Aligner (COEA) based on the combination of entity attributes and context. In this paper, we focus on (1) extracting attribute information and context of entities from the infobox of online encyclopedias and normalizing them, (2) computing the similarity of entity attributes based on Vector Space Model, and (3) further considering the entity similarity based on the topic model over entity context when the similarity of attributes is between the lower bound and the upper bound. Finally, data sets of entity alignment in online encyclopedias are constructed for simulation experiments. The experimental results, which show the method proposed in this paper outperforms traditional entity alignment algorithms, verify that our method can significantly improve the performance of entity alignment in online encyclopedias in the construction of Chinese knowledge graphs.
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This work is supported by the National Natural Science Foundation of China (61572353), the Natural Science Foundation of Tianjin (17JCYBJC15400), and the National Training Programs of Innovation and Entrepreneurship for Undergraduates (201910056374).
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Lv, Y., Wang, X., Yue, R., Tang, F., Xiang, X. (2019). COEA: An Efficient Method for Entity Alignment in Online Encyclopedias. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_57
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