XLink: An Unsupervised Bilingual Entity Linking System

  • Jing Zhang
  • Yixin Cao
  • Lei Hou
  • Juanzi LiEmail author
  • Hai-Tao Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)


Entity linking is a task of linking mentions in text to the corresponding entities in a knowledge base. Recently, entity linking has received considerable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on Wikipeida and Baidu Baike. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing and entity disambiguation. To eliminate dependency of language, we conduct mention parsing without any named entity recognition tools. To ensure the correctness of linking results, we propose an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation. Experiments show that our system gets a state-of-the-art performance and a high time efficiency.


Entity linking system Entity disambiguation Mention detection 



The work is supported by 973 Program (No. 2014CB340504), NSFC key project (No. 61533018, 61661146007), Fund of Online Education Research Center, Ministry of Education (No. 2016ZD102), THUNUS NExT Co-Lab, National Natural Science Foundation of China (Grant No. 61375054) and Natural Science Foundation of Guangdong Province (Grant No. 2014A030313745).


  1. 1.
    Aho, A.V., Corasick, M.J.: Efficient string matching: an aid to bibliographic search. Commun. ACM 18(6), 333–340 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Alhelbawy, A., Gaizauskas, R.J.: Graph ranking for collective named entity disambiguation. In: ACL, vol. 2, pp. 75–80 (2014)Google Scholar
  3. 3.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-76298-0_52 CrossRefGoogle Scholar
  4. 4.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250 (2008)Google Scholar
  5. 5.
    Cao, Y., Huang, L., Ji, H., Chen, X., Li, J.: Bridging text and knowledge by learning multi-prototype entity mention embedding. In: Proceedings of ACL (2017)Google Scholar
  6. 6.
    Cao, Y., Li, J., Guo, X., Bai, S., Ji, H., Tang, J.: Name list only? target entity disambiguation in short texts. EMNLP 15, 654–664 (2015)Google Scholar
  7. 7.
    Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 708–716 (2007)Google Scholar
  8. 8.
    Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J.A., et al.: Semtag and seeker: Bootstrapping the semantic web via automated semantic annotation. In: Proceedings of the 12th International Conference on World Wide Web, pp. 178–186 (2003)Google Scholar
  9. 9.
    Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with wikipedia pages. IEEE Softw. 29(1), 70–75 (2012)CrossRefGoogle Scholar
  10. 10.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370 (2005)Google Scholar
  11. 11.
    Han, X., Sun, L.: A generative entity-mention model for linking entities with knowledge base. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 945–954 (2011)Google Scholar
  12. 12.
    Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 782–792 (2011)Google Scholar
  13. 13.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8 (2011)Google Scholar
  14. 14.
    Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, pp. 233–242 (2007)Google Scholar
  15. 15.
    Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 509–518 (2008)Google Scholar
  16. 16.
    Pan, L., Wang, Z., Li, J., Tang, J.: Domain specific cross-lingual knowledge linking based on similarity flooding. In: Lehner, F., Fteimi, N. (eds.) KSEM 2016. LNCS, vol. 9983, pp. 426–438. Springer, Cham (2016). doi: 10.1007/978-3-319-47650-6_34 CrossRefGoogle Scholar
  17. 17.
    Pershina, M., He, Y., Grishman, R.: Personalized page rank for named entity disambiguation. In: HLT-NAACL, pp. 238–243 (2015)Google Scholar
  18. 18.
    Ratinov, L., Roth, D., Downey, D., Anderson, M.: Local and global algorithms for disambiguation to wikipedia. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1375–1384 (2011)Google Scholar
  19. 19.
    Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: Issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2015)CrossRefGoogle Scholar
  20. 20.
    Shirakawa, M., Wang, H., Song, Y., Wang, Z., Nakayama, K., Hara, T., Nishio, S.: Entity disambiguation based on a probabilistic taxonomy. Microsoft Research, Seattle, WA, USA, Tech. Rep. MSR-TR-2011-125 (2011)Google Scholar
  21. 21.
    Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW (2007)Google Scholar
  22. 22.
    Weston, J., Bordes, A., Chopra, S., Rush, A.M., van Merriënboer, B., Joulin, A., Mikolov, T.: Towards ai-complete question answering: a set of prerequisite toy tasks. arXiv preprint arXiv:1502.05698 (2015)
  23. 23.
    Yamada, I., Ito, T., Usami, S., Takagi, S., Takeda, H., Takefuji, Y.: Evaluating the helpfulness of linked entities to readers. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 169–178 (2014)Google Scholar
  24. 24.
    Yamada, I., Shindo, H., Takeda, H., Takefuji, Y.: Joint learning of the embedding of words and entities for named entity disambiguation. arXiv preprint arXiv:1601.01343 (2016)
  25. 25.
    Yao, X., Van Durme, B.: Information extraction over structured data: question answering with freebase. In: ACL, vol. 1, pp. 956–966 (2014)Google Scholar
  26. 26.
    Zhang, Y., Jin, H., Pan, L., Li, J.Z.: Rimom results for OAEI 2016. In: OM@ ISWC, pp. 210–216 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jing Zhang
    • 1
  • Yixin Cao
    • 1
  • Lei Hou
    • 1
  • Juanzi Li
    • 1
    Email author
  • Hai-Tao Zheng
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingPeople’s Republic of China

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