ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (2): 270-283.doi: 10.7544/issn1000-1239.2016.20150832

所属专题: 2016数据融合与知识融合专题

• 软件技术 • 上一篇    下一篇

基于图的中文集成实体链接算法

刘峤,钟云,李杨,刘瑶,秦志光   

  1. (电子科技大学信息与软件工程学院 成都 610054) (qliu@uestc.edu.cn)
  • 出版日期: 2016-02-01
  • 基金资助: 
    国家自然科学基金项目(61133016,61272527,61202445);教育部-中国移动科研基金项目(MCM20121041);中央高校基本科研业务费专项资金(ZYGX2014J066)

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

摘要: 实体链接(entity linking)是知识库扩容的核心关键技术,传统的实体链接方法通常受制于本地知识库的知识水平,而且忽略共现实体间的语义相关性.提出了一种基于图的中文集成实体链接方法,不仅能够充分利用知识库中实体间的结构化关系,而且能够通过增量证据挖掘获取外部知识,从而实现对同一文本中出现的多个歧义实体的批量实体链接.在开放域公开测试语料上的实验结果表明,所提出的实体相关图构造方法、增量证据挖掘方法和实体语义一致性判据是有效的,算法整体性能一致且显著地优于当前的主流算法.

关键词: 集成实体链接, 知识库扩容, 知识图谱, 实体相关图, 中文信息处理

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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