基于图的联合特征实体链接方法

doi:10.12066/j.issn.1007-2861.2068

Abstract

Abstract:

Entity linking refers to the process of linking entity mentioned in text with knowledge base entity, which is one of the key steps in knowledge graph and knowledge fusion. This paper proposes a graph-based method for multi-feature entity linking. This method first preprocesses the knowledge base and the text, then identifies the named entity references in the text, and then combines the semantic similarity of multiple features such as topics, context, metadata, etc. In the expanded graph model, the probability of restarting random walk is used, and the target candidate entity is selected by joint disambiguation. The results of experiment show that the joint feature-based entity linking method based on graphs effectively improves the effectiveness of entity linking.

Key words: entity linking, entity disambiguation, semantic relatedness, random walk with restart, natural language processing

CLC Number:

TP391

ZHOU Jin, ZHU Yonghua, ZHANG Tienan, XING Yixue, ZHANG Ke. A graph-based method for multi-feature entity linking[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(5): 747-755.

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References 19

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A graph-based method for multi-feature entity linking

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