Collective entity linking in web text: a graph-based method

  • Authors:

  • Xianpei Han;Le Sun;Jun Zhao

  • Affiliations:

  • Institute of Software, Chinese Academy of Sciences, Beijing, China;Institute of Software, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
  • Year:
  • 2011

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Abstract

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy. This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.