Abstract
Entity linking in queries is an important task for connecting search engines and knowledge bases. This task is very challenging because queries are usually very short and there is very limited context information for entity disambiguation. This paper proposes a new accurate and efficient entity linking approach for search queries. The proposed approach first jointly learns word, mention and entity embeddings in a unified space, and then computes a set of features for entity disambiguation based on the learned embeddings. The entity linking problem is solved as a ranking problem in our approach, a ranking SVM is trained to accurately predict entity links. Experiments on real data show that our proposed approach achieves better performance than comparison approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)
Blanco, R., Ottaviano, G., Meij, E.: Fast and space-efficient entity linking for queries. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (WSDM 2015), pp. 179–188, New York, NY, USA. ACM (2015)
Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. Mach. Learn. 94(2), 233–259 (2014)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Bordes, A., Weston, J., Collobert, R., Bengio, Y., et al.: Learning structured embeddings of knowledge bases. In: AAAI (2011)
Cheng, X., Roth, D.: Relational inference for wikification. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (2013)
Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems (I-Semantics) (2013)
Han, X., Sun, L., Zhao, J.: Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774 (2011)
Joachims, T.: Optimizing search engines using click through data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 133–142, New York, NY, USA. ACM (2002)
Joachims, T.: Training linear SVMS in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 217–226, New York, NY, USA. ACM (2006)
Kulkarni, S., Singh, A., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of wikipedia entities in web text. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 457–466 (2009)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Nineth AAAI Conference on Artificial Intelligence (2015)
Liu, X., Li, Y., Wu, H., Zhou, M., Wei, F., Lu, Y.: Entity linking for tweets. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013) (2013)
Mihalcea, R., Csomai, A.: Wikify! Linking documents to encyclopedic knowledge. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 233–242 (2007)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates Inc. (2013)
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)
Radhakrishnan, P., Bansal, R., Gupta, M., Varma, V.: Exploiting wikipedia inlinks for linking entities in queries. In: Proceedings of the First International Workshop on Entity Recognition & #38; Disambiguation (ERD 2014), pp. 101–104, New York, NY, USA. ACM (2014)
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 (HLT 2011), vol. 1, pp. 1375–1384, Stroudsburg, PA, USA. Association for Computational Linguistics (2011)
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)
Shen, W., Wang, J., Luo, P., Wang, M.: LIEGE: link entities in web lists with knowledge base. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1424–1432 (2012)
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, pp. 449–458 (2012)
Shen, W., Wang, J., Luo, P., Wang, M.: Linking named entities in tweets with knowledge base via user interest modeling. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013), pp. 68–76, New York, NY, USA. ACM (2013)
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems, pp. 926–934 (2013)
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1112–1119 (2014)
Acknowledgement
The work is supported by NSFC (No. 61772079) and the project of Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wang, Z., Wang, R., Wen, D., Huang, Y., Li, C. (2017). Entity Linking in Queries Using Word, Mention and Entity Joint Embedding. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-70682-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70681-8
Online ISBN: 978-3-319-70682-5
eBook Packages: Computer ScienceComputer Science (R0)