Linking Entities in Short Texts Based on a Chinese Semantic Knowledge Base

  • Yi Zeng
  • Dongsheng Wang
  • Tielin Zhang
  • Hao Wang
  • Hongwei Hao
Part of the Communications in Computer and Information Science book series (CCIS, volume 400)

Abstract

Populating existing knowledge base with new facts is important to keep the knowledge base fresh and most updated. Before importing new knowledge into the knowledge base, entity linking is required so that the entities in the new knowledge can be linked to the entities in the knowledge base. During this process, entity disambiguation is the most challenging task. There have been many studies on leveraging name ambiguity problem via a variety of algorithms. In this paper, we propose an entity linking method based on Chinese Semantic Knowledge where entity disambiguation can be addressed by retrieving a variety of semantic relations and analyzing the corresponding documents with similarity measurement. Based on the proposed method, we developed CASIA_EL, a system for linking entities with knowledge bases. We validate the proposed method by linking 1232 entities mined from Sina Weibo to a Chinese Semantic knowledge base, resulting in an accuracy of 88.5%. The results show that the CASIA_EL system and the proposed algorithm are potentially effective.

Keywords

Entity linking Chinese Semantic Knowledge Semantic similarity Entity disambiguation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: A Large Ontology from Wikipedia and WordNet. Journal of Web Semantics 6(3), 203–217 (2008)CrossRefGoogle Scholar
  2. 2.
    Bagga, A., Baldwin, B.: Entity-based Cross-document Coreferencing Using the Vector Space Model. In: Proceedings of the 17th International Conference on Computational Linguistics (COLING 1998), pp. 79–85. ACL, Montreal (1998)CrossRefGoogle Scholar
  3. 3.
    Mann, G.S., Yarowsky, D.: Unsupervised personal name disambiguation. In: Proceedings of the 7th Conference on Natural Language Learning (CONLL 2003), pp. 33–40. ACL, Edmonton (2003)CrossRefGoogle Scholar
  4. 4.
    Bekkerman, R., McCallum, A.: Disambiguating Web appearances of people in a social network. In: Proceedings of the 14th International Conference on the World Wide Web (WWW 2005), pp. 463–470. ACM Press, Chiba (2005)CrossRefGoogle Scholar
  5. 5.
    Jiang, L., Wang, J., An, N., Wang, S., Zhan, J., Li, L.: GRAPE: A Graph-Based Framework for Disambiguating People Appearances in Web Search. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM 2009), pp. 199–208. IEEE Press (2009)Google Scholar
  6. 6.
    Han, X., Zhao, J.: Named entity disambiguation by leveraging Wikipedia semantic knowledge. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM 2009), pp. 215–224. ACM Press, Hong Kong (2009)CrossRefGoogle Scholar
  7. 7.
    Shen, W., Wang, J., Luo, P., Wang, M.: LINDEN: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st International Conference on the World Wide Web (WWW 2012), pp. 449–458. ACM Press, Lyon (2012)CrossRefGoogle Scholar
  8. 8.
    Niu, X., Sun, X., Wang, H., Rong, S., Qi, G., Yu, Y.: Zhishi.me - Weaving Chinese Linking Open Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 205–220. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Wang, Z., Li, J., Wang, Z., Tang, J.: Cross-lingual Knowledge Linking across Wiki Knowledge Bases. In: Proceedings of the 21st World Wide Web Conference (WWW 2012), pp. 459–468. ACM Press, Lyon (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yi Zeng
    • 1
  • Dongsheng Wang
    • 1
  • Tielin Zhang
    • 1
  • Hao Wang
    • 1
  • Hongwei Hao
    • 1
  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina

Personalised recommendations