Entity Translation with Collective Inference in Knowledge Graph

Conference paper

DOI: 10.1007/978-3-319-25207-0_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9362)
Cite this paper as:
Li Q., Liu S., Lin R., Li M., Zhou M. (2015) Entity Translation with Collective Inference in Knowledge Graph. In: Li J., Ji H., Zhao D., Feng Y. (eds) Natural Language Processing and Chinese Computing. Lecture Notes in Computer Science, vol 9362. Springer, Cham


Nowadays knowledge base (KB) has been viewed as one of the important infrastructures for many web search applications and NLP tasks. However, in practice the availability of KB data varies from language to language, which greatly limits potential usage of knowledge base. In this paper, we propose a novel method to construct or enrich a knowledge base by entity translation with help of another KB but compiled in a different language. In our work, we concentrate on two key tasks: 1) collecting translation candidates with as good coverage as possible from various sources such as web or lexicon; 2) building an effective disambiguation algorithm based on collective inference approach over knowledge graph to find correct translation for entities in the source knowledge base. We conduct experiments on movie domain of our in-house knowledge base from English to Chinese, and the results show the proposed method can achieve very high translation precision compared with classical translation methods, and significantly increase the volume of Chinese knowledge base in this domain.


Knowledge base Machine translation Collective learning 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qinglin Li
    • 1
  • Shujie Liu
    • 2
  • Rui Lin
    • 3
  • Mu Li
    • 2
  • Ming Zhou
    • 2
  1. 1.Shanghai Jiaotong UniversityShanghaiChina
  2. 2.Microsoft Research AsiaBeijingChina
  3. 3.Harbin Institute of TechnologyHarbinChina

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