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Collective Entity Linking on Relational Graph Model with Mentions

  • Jing Gong
  • Chong FengEmail author
  • Yong Liu
  • Ge Shi
  • Heyan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.

Keywords

Collective entity linking Entity disambiguation Relational graph 

Notes

Acknowledgement

The research of this paper is partially supported by National 863 project 2015AA015404 and open project of State key lab. Smart manufacturing for special vehicles and transmission system.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jing Gong
    • 1
  • Chong Feng
    • 1
    Email author
  • Yong Liu
    • 2
  • Ge Shi
    • 1
  • Heyan Huang
    • 1
  1. 1.Beijing Institute of Technology UniversityBeijingChina
  2. 2.State Key LabBeijingChina

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