Neural Processing Letters

, Volume 50, Issue 2, pp 1861–1875 | Cite as

Measuring Entity Relatedness via Entity and Text Joint Embedding

  • Weixin Zeng
  • Jiuyang Tang
  • Xiang ZhaoEmail author


As unique identifiers of objects and basic components of knowledge graphs, entities are crucial to many natural language processing related works, such as entity linking and question answering, in which the estimation of entity relatedness is required. Current entity relatedness measures either consider entities as words, which neglects the rich semantics entities contain, or are integrated into extrinsic applications, which fail to evaluate the intrinsic effectiveness. In this work, we propose E5, an effective entity relatedness measure taking into account of entity description text in a neural embedding manner. We first jointly map words and entities to the same high-dimensional vector space, the output of which is utilized as the input for the following joint entity and text embedding training. The well-trained entity and text embedding network is then leveraged to measure similarity between entities and entity descriptions, which in combination with a graph structure based method, constitute the eventual entity relatedness measure. The experimental results validate the usefulness of E5.


Entity relatedness Joint embedding Neural embedding network Word embedding 



The authors would like to thank the anonymous reviewers for their insightful and constructive comments, which greatly contributed to improving the quality of the paper. This work was partially supported by NSFC under Grants Nos. 61872446 and 71690233.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Science and Technology on Information System EngineeringNational University of Defense TechnologyChangshaChina
  2. 2.Collaborative Innovation Center of Geospatial TechnologyWuhanChina

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