Skip to main content
SpringerLink
Log in

Entity Linking in Enterprise Search: Combining Textual and Structural Information

  • Chapter
  • First Online:
Linking and Mining Heterogeneous and Multi-view Data

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Fast and correct identification of named entities in queries is crucial for query understanding and to map the query to information in structured knowledge base. Most of the existing works have focused on utilizing search logs and manually curated knowledge bases for entity linking and often involve complex graph operations and are generally slow. We describe a simple, yet fast and accurate, probabilistic entity linking algorithm that can be used in enterprise settings where automatically constructed, domain-specific knowledge graphs are used. In addition to the linked graph structure, textual evidence from the domain-specific corpus is also utilized to improve the performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    For domain-specific applications where the knowledge graph is constructed using automated methods, the set of input documents constitute the background corpus. For applications that use generic, open-domain knowledge bases such as DBPedia and WikiData, Wikipedia could be used as the background text corpus.

  2. 2.

    http://opennlp.apache.org/.

  3. 3.

    https://nlp.stanford.edu/software/CRF-NER.html.

  4. 4.

    https://www.ibm.com/watson/services/natural-language-understanding/.

  5. 5.

    Text context components can be computed by using an inverted index implementation where using the context terms as queries, most relevant mention docs (and thus the corresponding entities) can be retrieved in a single query. Likewise, entity context component can be computed by just counting the number of connections between target entities—can be performed in a single optimized SQL query.

References

  1. Aggarwal, N., Buitelaar, P.: Wikipedia-based distributional semantics for entity relatedness. In: 2014 AAAI Fall Symposium Series (2014)

    Google Scholar 

  2. 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.F., Allemang, D., Lee, K.I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) 6th International Semantic Web Conference (ISWC 2007). Lecture Notes in Computer Science, vol. 4825, pp. 722–735. Busan (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  3. Aula, A., Khan, R.M., Guan, Z.: How does search behavior change as search becomes more difficult? In: Mynatt, E.D., Schoner, D., Fitzpatrick, G., Hudson, S.E., Edwards, W.K., Rodden, T. (eds.) Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, Atlanta, GA, 10–15 April 2010, pp. 35–44. Association for Computing Machinery, New York (2010). http://doi.acm.org/10.1145/1753326.1753333

    Google Scholar 

  4. Bagga, A., Baldwin, B.: Entity-based cross-document coreferencing using the vector space model. In: Boitet, C., Whitelock, P. (eds.) ACL/COLING, pp. 79–85. Morgan Kaufmann Publishers/ACL (1998). http://aclweb.org/anthology/P/P98/

  5. Bhatia, S., Jain, A.: Context sensitive entity linking of search queries in enterprise knowledge graphs. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenic, D., Auer, S., Lange, C. (eds.) The Semantic Web – ESWC 2016 Satellite Events, Heraklion, Crete, 29 May–2 June 2016, Revised Selected Papers. Lecture Notes in Computer Science, vol. 9989, pp. 50–54 (2016). https://doi.org/10.1007/978-3-319-47602-5_11

    Article  Google Scholar 

  6. Bhatia, S., Vishwakarma, H.: Know Thy Neighbors, and More! Studying the Role of Context in Entity Recommendation. In: HT ’18: 29th ACM Conference on Hypertext and Social Media, 9–12 July 2018, Baltimore. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3209542.3209548

  7. Bhatia, S., Majumdar, D., Mitra, P.: Query suggestions in the absence of query logs. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11, pp. 795–804. Association for Computing Machinery, New York (2011). http://doi.acm.org/10.1145/2009916.2010023

  8. Bhatia, S., Rajshree, N., Jain, A., Aggarwal, N.: Tools and infrastructure for supporting enterprise knowledge graphs. In: Cong, G., Peng, W., Zhang, W.E., Li, C., Sun, A. (eds.) Proceedings of the 13th International Conference Advanced Data Mining and Applications, ADMA 2017, Singapore, 5–6 November 2017. Lecture Notes in Computer Science, vol. 10604, pp. 846–852. Springer, Berlin (2017). https://doi.org/10.1007/978-3-319-69179-4_60

    Google Scholar 

  9. Blanco, R., Cambazoglu, B.B., Mika, P., Torzec, N.: Entity recommendations in web search. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) The Semantic Web – ISWC 2013, pp. 33–48. Springer, Berlin (2013)

    Google Scholar 

  10. Blanco, R., Ottaviano, G., Meij, E.: Fast and space-efficient entity linking for queries. In: Cheng, X., Li, H., Gabrilovich, E., Tang, J. (eds.) Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM 2015, Shanghai, 2–6 February 2015, pp. 179–188. Association for Computing Machinery, New York (2015). http://dl.acm.org/citation.cfm?id=2684822

    Google Scholar 

  11. Brizan, D.G., Tansel, A.U.: A. survey of entity resolution and record linkage methodologies. Commun. IIMA 6(3), 5 (2006)

    Google Scholar 

  12. Castelli, V., Raghavan, H., Florian, R., Han, D.J., Luo, X., Roukos, S.: Distilling and exploring nuggets from a corpus. In: SIGIR, pp. 1006–1006 (2012)

    Google Scholar 

  13. Cheng, X., Roth, D.: Relational inference for wikification. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1787–1796. Association for Computational Linguistics, Seattle (2013). http://aclweb.org/anthology/D/D13/D13-1184.pdf

  14. Christen, P.: Data Matching – Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Data-Centric Systems and Applications. Springer, Berlin (2012)

    Google Scholar 

  15. Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Eisner, J. (ed.) EMNLP-CoNLL 2007, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 28–30 June 2007, Prague, pp. 708–716. Association for Computational Linguistics, Seattle (2007). http://www.aclweb.org/anthology/K/K07/

    Google Scholar 

  16. Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR ’14, pp. 365–374. Association for Computing Machinery, New York (2014). http://doi.acm.org/10.1145/2600428.2609628

  17. Dill, S., Eiron, N., Gibson, D., Gruhl, D., Guha, R., Jhingran, A., Kanungo, T., Rajagopalan, S., Tomkins, A., Tomlin, J.A., Zien, J.Y.: Semtag and seeker: Bootstrapping the semantic web via automated semantic annotation. In: Proceedings of the 12th International Conference on World Wide Web, WWW ’03, pp. 178–186. Association for Computing Machinery, New York (2003). http://doi.acm.org/10.1145/775152.775178

  18. Dunn, H.L.: Record linkage. Am. J. Public Health and the Nations Health 36(12), 1412–1416 (1946). https://doi.org/10.2105/AJPH.36.12.1412. PMID: 18016455

    Article  Google Scholar 

  19. Elango, P.: Coreference resolution: A survey. Technical Report, University of Wisconsin, Madison, WI (2005)

    Google Scholar 

  20. Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with Wikipedia pages. IEEE Softw. 29(1), 70–75 (2012). http://dx.doi.org/10.1109/MS.2011.122

    Article  Google Scholar 

  21. Gottipati, S., Jiang, J.: Linking entities to a knowledge base with query expansion. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’11, pp. 804–813. Association for Computational Linguistics, Stroudsburg (2011). http://dl.acm.org/citation.cfm?id=2145432.2145523

  22. Guha, R., McCool, R.: Tap: a semantic web test-bed. Web Semant. Sci. Serv. Agents on the World Wide Web 1(1), 81–87 (2003). https://doi.org/10.1016/j.websem.2003.07.004. http://www.sciencedirect.com/science/article/pii/S1570826803000064

    Article  Google Scholar 

  23. Guo, S., Chang, M.W., Kiciman, E.: To link or not to link? a study on end-to-end tweet entity linking. In: Vanderwende, L., III, H.D., Kirchhoff, K. (eds.) Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, 9–14 June 2013, Westin Peachtree Plaza Hotel, Atlanta, pp. 1020–1030. The Association for Computational Linguistics (2013). http://aclweb.org/anthology/N/N13/N13-1122.pdf

  24. Hasibi, F., Balog, K., Bratsberg, S.E.: Entity linking in queries: tasks and evaluation. In: Proceedings of the 2015 International Conference on The Theory of Information Retrieval, ICTIR ’15, pp. 171–180. Association for Computing Machinery, New York (2015). http://doi.acm.org/10.1145/2808194.2809473

  25. Hasibi, F., Balog, K., Bratsberg, S.E.: Entity linking in queries: efficiency vs. effectiveness. In: Jose, J.M., Hauff, C., Altingövde, I.S., Song, D., Albakour, D., Watt, S.N.K., Tait, J. (eds.) Proceedings of the 39th European Conference on IR Research Advances in Information Retrieval, ECIR 2017, Aberdeen, 8–13 April 2017. Lecture Notes in Computer Science, vol. 10193, pp. 40–53 (2017)

    Article  Google Scholar 

  26. 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: EMNLP, pp. 782–792. Association for Computational Linguistics, Seattle (2011). http://www.aclweb.org/anthology/D11-1072

  27. Hoffart, J., Seufert, S., Nguyen, D.B., Theobald, M., Weikum, G.: Kore: keyphrase overlap relatedness for entity disambiguation. In: Chen, X.-W., Lebanon, G., Wang, H., Zaki, M.J. (eds.) 21st ACM International Conference on Information and Knowledge Management, CIKM’12, Maui, 29 October–02 November 2012, pp. 545–554. Association for Computing Machinery, New York (2012). http://dl.acm.org/citation.cfm?id=2396761

    Google Scholar 

  28. Huang, J., Treeratpituk, P., Taylor, S.M., Giles, C.L.: Enhancing cross document coreference of web documents with context similarity and very large scale text categorization. In: Huang, C.R., Jurafsky, D. (eds.) COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23–27 August 2010, Beijing, pp. 483–491. Tsinghua University Press (2010). http://aclweb.org/anthology/C/C10/

  29. Khalid, M.A., Jijkoun, V., de Rijke, M.: The impact of named entity normalization on information retrieval for question answering. Springer, New York (2009). http://dare.uva.nl/record/297954

    Google Scholar 

  30. Kulkarni, S., 0003, A.S., Ramakrishnan, G., Chakrabarti, S.: Collective annotation of Wikipedia entities in web text. In: IV, J.F.E., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.) Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, 28 June–1 July, 2009, pp. 457–466. Association for Computing Machinery, New York (2009). http://doi.acm.org/10.1145/1557019.1557073

  31. Lin, T., Pantel, P., Gamon, M., Kannan, A., Fuxman, A.: Active objects: Actions for entity-centric search. In: World Wide Web. Association for Computing Machinery, New York (2012). http://research.microsoft.com/apps/pubs/default.aspx?id=161389

  32. Liu, X., Li, Y., Wu, H., Zhou, M., Wei, F., Lu, Y.: Entity linking for tweets. In: ACL (1), pp. 1304–1311. The Association for Computer Linguistics (2013). http://aclweb.org/anthology/P/P13/

  33. 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. Association for Computing Machinery, New York (2011)

    Google Scholar 

  34. Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In: Silva, M.J., Laender, A.H.F., Baeza-Yates, R.A., McGuinness, D.L., Olstad, B., Olsen, Ø.H., Falcão, A.O. (eds.) Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, Lisbon, 6–10 November 2007, pp. 233–242. Association for Computing Machinery, New York (2007). http://doi.acm.org/10.1145/1321440.1321475

    Google Scholar 

  35. Mohit, B.: Named entity recognition, pp. 221–245 (2014). https://doi.org/10.1007/978-3-642-45358-8_7

    Chapter  Google Scholar 

  36. Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. Trans. Assoc. Comput. Linguist. 2, 231–244 (2014). https://transacl.org/ojs/index.php/tacl/article/view/291

    Google Scholar 

  37. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticæ Investigationes 30(1), 3–26 (2007). http://www.jbe-platform.com/content/journals/10.1075/li.30.1.03nad

    Article  Google Scholar 

  38. Nagarajan, M., Wilkins, A.D., Bachman, B.J., Novikov, I.B., Bao, S., Haas, P.J., Terrón-Díaz, M.E., Bhatia, S., Adikesavan, A.K., Labrie, J.J., Regenbogen, S., Buchovecky, C.M., Pickering, C.R., Kato, L., Lisewski, A.M., Lelescu, A., Zhang, H., Boyer, S., Weber, G., Chen, Y., Donehower, L.A., Spangler, W.S., Lichtarge, O.: Predicting future scientific discoveries based on a networked analysis of the past literature. In: Cao, L., Zhang, C., Joachims, T., Webb, G.I., Margineantu, D.D., Williams, G. (eds.) Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, 10–13 August 2015, pp. 2019–2028. Association for Computing Machinery, New York (2015). http://dl.acm.org/citation.cfm?id=2783258

  39. Newcombe, H.B., Kennedy, J.M., Axford, S.J., James, A.P.: Automatic linkage of vital records. Science 130(3381), 954–959 (1959). http://science.sciencemag.org/content/130/3381/954

    Article  Google Scholar 

  40. Pang, B., Kumar, R.: Search in the lost sense of “query”: question formulation in web search queries and its temporal changes. In: ACL (Short Papers), pp. 135–140. The Association for Computer Linguistics (2011). http://www.aclweb.org/anthology/P11-2024

  41. Popescu, O.: Dynamic parameters for cross document coreference. In: Huang, C.R., Jurafsky, D. (eds.) COLING 2010, 23rd International Conference on Computational Linguistics, Posters Volume, 23–27 August 2010, Beijing, pp. 988–996. Chinese Information Processing Society of China (2010). http://aclweb.org/anthology/C/C10/C10-2114.pdf

  42. Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 771–780. Association for Computing Machinery, New York (2010). http://doi.acm.org/10.1145/1772690.1772769

  43. 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 ’11, vol. 1, pp. 1375–1384. Association for Computational Linguistics, Stroudsburg (2011). http://dl.acm.org/citation.cfm?id=2002472.2002642

  44. Shen, W., Wang, J., Luo, P., Wang, M.: Linking named entities in tweets with knowledge base via user interest modeling. In: Dhillon, I.S., Koren, Y., Ghani, R., Senator, T.E., Bradley, P., Parekh, R., He, J., Grossman, R.L., Uthurusamy, R. (eds.) The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, 11–14 August 2013, pp. 68–76. Association for Computing Machinery, New York (2013). http://dl.acm.org/citation.cfm?id=2487575

    Google Scholar 

  45. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pp. 697–706. Association for Computing Machinery, New York (2007). http://doi.acm.org/10.1145/1242572.1242667

  46. Varma, V., Bysani, P., Reddy, K., Reddy, V.B., Kovelamudi, S., Vaddepally, S.R., Nanduri, R., Kumar, N.K., Gsk, S., Pingali, P.: IIIT Hyderabad in guided summarization and knowledge base population. In: TAC. NIST (2010). http://www.nist.gov/tac/publications/2010/papers.html

  47. Welty, C., Murdock, J.W., Kalyanpur, A., Fan, J.: A comparison of hard filters and soft evidence for answer typing in Watson. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) The Semantic Web – ISWC 2012, pp. 243–256. Springer, Berlin (2012)

    Chapter  Google Scholar 

  48. West, R., Gabrilovich, E., Murphy, K., Sun, S., Gupta, R., Lin, D.: Knowledge base completion via search-based question answering. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 515–526. Association for Computing Machinery, New York (2014)

    Google Scholar 

  49. Zhang, W., Su, J., Tan, C.L., Wang, W.: Entity linking leveraging automatically generated annotation. In: Huang, C.R., Jurafsky, D. (eds.) COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, 23–27 August 2010, Beijing, pp. 1290–1298. Tsinghua University Press (2010). http://aclweb.org/anthology/C/C10/

  50. Zheng, Z., Li, F., Huang, M., Zhu, X.: Learning to link entities with knowledge base. In: HLT-NAACL, pp. 483–491. The Association for Computational Linguistics (2010). http://www.aclweb.org/anthology/N10-1072

Download references

Author information

Authors and Affiliations

  1. IBM Research AI, New Delhi, India

    Sumit Bhatia

Authors
  1. Sumit Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sumit Bhatia .

Editor information

Editors and Affiliations

  1. Queen’s University Belfast, Northern Ireland, UK

    Deepak P

  2. Queen’s University Belfast, Northern Ireland, UK

    Anna Jurek-Loughrey

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bhatia, S. (2019). Entity Linking in Enterprise Search: Combining Textual and Structural Information. In: P, D., Jurek-Loughrey, A. (eds) Linking and Mining Heterogeneous and Multi-view Data. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-01872-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01872-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01871-9

  • Online ISBN: 978-3-030-01872-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation