Advertisement

Frontiers of Computer Science

, Volume 14, Issue 2, pp 291–303 | Cite as

Graph-ranking collective Chinese entity linking algorithm

  • Tao XieEmail author
  • Bin Wu
  • Bingjing Jia
  • Bai Wang
Research Article
  • 89 Downloads

Abstract

Entity linking (EL) systems aim to link entity mentions in the document to their corresponding entity records in a reference knowledge base. Existing EL approaches usually ignore the semantic correlation between the mentions in the text, and are limited to the scale of the local knowledge base. In this paper, we propose a novel graphranking collective Chinese entity linking (GRCCEL) algorithm, which can take advantage of both the structured relationship between entities in the local knowledge base and the additional background information offered by external knowledge sources. By improved weighted word2vec textual similarity and improved PageRank algorithm, more semantic information and structural information can be captured in the document. With an incremental evidence mining process, more powerful discrimination capability for similar entities can be obtained. We evaluate the performance of our algorithm on some open domain corpus. Experimental results show the effectiveness of our method in Chinese entity linking task and demonstrate the superiority of our method over state-of-the-art methods.

Keywords

collective entity linking knowledge mapping word embedding entity correlation graph PageRank 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

This work was supported in part by the National Basic Research (973) Program of China (2013CB329606) and the Natural Science Research Program of Anhui Science and Technology University (ZRC2016494).

Supplementary material

11704_2018_7175_MOESM1_ESM.pdf (333 kb)
Graph-ranking collective Chinese entity linking algorithm

References

  1. 1.
    Huai, B X, Bao, T F, Zhu, H S, Liu, Q. Topic modeling approach to named entity linking. Journal of Software, 2014, 21(8): 1235–1248Google Scholar
  2. 2.
    Xiao L, Weld, D S. Fine-grained entity recognition. In: Proceedings of AAAI Conference on Artificial Intelligence. 2012, 1189–1192Google Scholar
  3. 3.
    Wu, F, Weld, D S. Open information extraction using Wikipedia. In: Proceedings of the 48th AnnualMeeting of the Association for Computational Linguistics. Association for Computational Linguistics. 2013, 118–127Google Scholar
  4. 4.
    Wu, F, Weld, D S. Autonomously semantifying Wikipedia. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. 2007, 41–50Google Scholar
  5. 5.
    Ji, H, Grishman, R. Knowledge base population: successful approaches and challenges. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Lanuoge Technology- Volume 1. Association for Computational Linguistics. 2011, 1148–1158Google Scholar
  6. 6.
    Dredze, M, Mcnamee, P, Rao, D, Gerber, A, Finin, T. Entity disambiguation for knowledge base population. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 2010, 277–285Google Scholar
  7. 7.
    Shen, W, Wang, J, Han, J. Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Transactions on Knowledge & Data Engineering, 2015, 27(2): 443–460CrossRefGoogle Scholar
  8. 8.
    Li, X, Strassel, S M, Ji, H, Griffitt, K, Ellis, J. Linguistic resources for entity linking evaluation: from monolingual to cross-lingual. In: Proceedings of International Conference on Language Resources and Evaluation. 2013, 3098–3105Google Scholar
  9. 9.
    Bunescu, R C, Pasca, M. Using encyclopedic knowledge for named entity disambiguation. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics. 2006, 9–16Google Scholar
  10. 10.
    Cucerzan, S. Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007, 708–716Google Scholar
  11. 11.
    Yang, Z, Huang, H. WSD method based on heterogeneous relation graph. Journal of Computer Research & Development, 2013, 50(2): 437–444Google Scholar
  12. 12.
    Cucerzan, S. Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2007, 708–716Google Scholar
  13. 13.
    Nguyen, H T, Cao, T H. Exploring Wikipedia and text features for named entity disambiguation. In: Proceedings of Asian Conference on Intelligent Information and Database Systems. 2010, 11–20CrossRefGoogle Scholar
  14. 14.
    Zeng, Y, Wang, D, Zhang, T, Wang, H, Hao, H. Linking entities in short texts based on a Chinese semantic knowledge base. In: Proceedings of Natural Language Processing and Chinese Computing: Second CCF Conference. 2013, 266–276CrossRefGoogle Scholar
  15. 15.
    Zhang, T, Liu, K, Zhao, J. A graph-based similarity measure between Wikipedia concepts and its applications in entity linking system. Journal of Chinese Information Processings, 2015, 22(1): 58–67Google Scholar
  16. 16.
    Zuo, Z, Kasneci, G, Gruetze, T, Naumann, F. BEL: bagging for entity linking. In: Proceedings of International Conference on Computational Linguistics. 2014, 266–276Google Scholar
  17. 17.
    Xu, J, Gan, L, Zhou, B, Wu, Q. An unsupervised method for linking entity mentions in Chinese text. In: Proceedings of Asia-Pacific Services Computing Conference. 2016, 183–195Google Scholar
  18. 18.
    Han, X, Sun, L, Zhao, J. Collective entity linking in web text: a graphbased method. In: Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 765–774Google Scholar
  19. 19.
    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 World Wide Web. 2012, 449–458Google Scholar
  20. 20.
    Hoffart, J, Yosef, M A, Bordino, I, Fürstenau, H. Robust disambiguation of named entities in text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 782–792Google Scholar
  21. 21.
    Guo, Z, Barbosa, D. Robust entity linking via random walks. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. 2014, 499–508Google Scholar
  22. 22.
    Moro, A, Raganato, A, Navigli, R. Entity linking meets word sense disambiguation: a unified approach. Journal of Transactions of the Association for Computational Linguistics, 2014, 22(5): 231–244CrossRefGoogle Scholar
  23. 23.
    Li, Y, Wang, C, Han, F, Han, J, Dan, R. Mining evidences for named entity disambiguation. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1070–1078CrossRefGoogle Scholar
  24. 24.
    Mikolov, T, Sutskever, I, Chen, K, Corrado, G, Dean, J. Distributed representations of words and phrases and their compositionality. Journal of Advances in Neural Information Processing Systems, 2013, 26: 3111–3119Google Scholar
  25. 25.
    Alhelbawy, A, Gaizauskas, R. Graph ranking for collective named entity disambiguation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014, 75–80Google Scholar
  26. 26.
    Hachey, B, Radford, W, Curran, J R. Graph-based named entity linking with Wikipedia. In: Proceedings of International Conference on Web Information Systems Engineering. 2011, 213–226Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Telecommunications Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations