ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (2): 270-283.doi: 10.7544/issn1000-1239.2016.20150832

Special Issue: 2016数据融合与知识融合专题

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Graph-Based Collective Chinese Entity Linking Algorithm

Liu Qiao, Zhong Yun, Li Yang, Liu Yao, Qin Zhiguang   

  1. (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054)
  • Online:2016-02-01

Abstract: Entity Linking technology is a central concern of the knowledge base population research area. Traditional entity linking methods are usually limited by the immaturity of the local knowledge base, and deliberately ignore the semantic correlation between the mentions that co-occurr within a text corpus. In this work, we propose a novel graph-based collective entity linking algorithm for Chinese information processing, which not only can take full advantage of the structured relationship of the entities offered by the local knowledge base, but also can make use of the additional background information offered by external knowledge sources. Through an incremental evidence minning process, the algorithm achieves the goal of linking the mentions that are extraced from the text corpus, with their corresponding entities located in the local knowledge base in a batch manner. Experimental results on some open domain corpus demonstrate the validity of the proposed referent graph construction method, the incremental evidence minning process, and the coherence criterion between the mention-entity pairs. Experimental evidences show that the proposed entity linking algorithm consistently outperforms other state-of-the-art algorithms.

Key words: collective entity linking, knowledge base population, knowledge graph, referent graph, Chinese information processing

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