Skip to main content
SpringerLink
Log in
Book cover

Clusters, Orders, and Trees: Methods and Applications pp 341–376Cite as

Improving Web Search Relevance with Learning Structure of Domain Concepts

  • Chapter
  • First Online:

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 92))

Abstract

This paper addresses the problem of improving the relevance of a search engine results in a vertical domain. The proposed algorithm is built on a structured taxonomy of keywords. The taxonomy construction process starts from the seed terms (keywords) and mines the available source domains for new terms associated with these entities. These new terms are formed in several steps. First the snippets of answers generated by the search engine are parsed producing parsing trees. Then commonalities of these parsing trees are found by using a machine learning algorithm. These commonality expressions then form new keywords as parameters of existing keywords and are turned into new seeds at the next learning iteration. To match NL expressions between source and target domains, the proposed algorithm uses syntactic generalization, an operation which finds a set of maximal common sub-trees of constituency parse trees of these expressions. The evaluation study of the proposed method revealed the improvement of search relevance in vertical and horizontal domains. It had shown significant contribution of the learned taxonomy in a vertical domain and a noticeable contribution of a hybrid system (that combines of taxonomy and syntactic generalization) in the horizontal domains. The industrial evaluation of a hybrid system reveals that the proposed algorithm is suitable for integration into industrial systems. The algorithm is implemented as a component of Apache OpenNLP project.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
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

Learn about institutional subscriptions

References

  1. Allen, J.F.: Natural Language Understanding. Benjamin Cummings, Menlo Park (1987)

    Google Scholar 

  2. Abney, S.: “Parsing by Chunks”, Principle-Based Parsing, Kluwer Academic Publishers, pp. 257–278 (1991)

    Google Scholar 

  3. Blanco-Fernández, Y., López-Nores, M., Pazos-Arias, J.J., Garc’ıa-Duque, J.: An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles. Eng. Appl. Artif. Intell. 24(8), 1385–1397 (2011)

    Article  Google Scholar 

  4. Buitelaar, P., Olejnik, D., Sintek, M.: A proteg’e’ plug-in for ontology extraction from text based on linguistic analysis. In: Proceedings of the International Semantic Web Conference (ISWC) (2003)

    Google Scholar 

  5. De la Rosa, J.L., Rovira, M., Beer, M., Montaner, M., Gibovic, D.: Reducing administrative burden by online information and referral services. In: Reddick, C.G. (ed.) Citizens and E-Government: Evaluating Policy and Management, pp. 131–157. IGI Global, Austin (2010)

    Chapter  Google Scholar 

  6. Dzikovska, M., Swift, M., Allen, J., de Beaumont, W.: Generic parsing for multi-domain semantic interpretation. In: International Workshop on Parsing Technologies (Iwpt05), Vancouver (2005)

    Google Scholar 

  7. Galitsky, B.: Natural Language Question Answering System: Technique of Semantic Headers. Advanced Knowledge International, Adelaide (2003)

    Google Scholar 

  8. Galitsky, B.: Machine learning of syntactic parse trees for search and classification of text. Eng. Appl. Artif. Intell. 26(3), 1072–1091 (2013)

    Article  Google Scholar 

  9. Galitsky, B., Dobrocsi, G., de la Rosa, J.L., Kuznetsov, S.O.: From generalization of syntactic parse trees to conceptual graphs. In: 18th International Conference on Conceptual Structures (ICCS), pp. 185–190 (2010)

    Google Scholar 

  10. Galitsky, B.A., Kovalerchuk, B., de la Rosa, J.L.: Assessing plausibility of explanation and meta-explanation in inter-human conflicts. A special issue on semantic-based information and engineering systems. Eng. Appl. Artif. Intell. 24(8), 1472–1486 (2011)

    Google Scholar 

  11. Galitsky, B., Dobrocsi, G., de la Rosa, J.L., Kuznetsov, S.O.: Using Generalization of syntactic parse trees for taxonomy capture on the web. In: 19th International Conference on Conceptual Structures (ICCS), pp. 104–117 (2011)

    Google Scholar 

  12. Galitsky, B., Dobrocsi, G., de la Rosa, J.L.: Inferring semantic properties of sentences mining syntactic parse trees. Data Knowl. Eng. 81–82, 21–45 (2012)

    Article  Google Scholar 

  13. Galitsky, B., González, M.P, Chesñevar C.I.: A novel approach for classifying customer complaints through graphs similarities in argumentative dialogue. Decision Support Systems 46(3), 717–729 (2009)

    Article  Google Scholar 

  14. Gildea, D.: Loosely tree-based alignment for machine translation. In Proceedings of the 41th Annual Conference of the Association for Computational Linguistics (ACL-03), pp. 80–87, Sapporo, Japan (2003)

    Google Scholar 

  15. Harris, Z.: Mathematical Structures of Language. Wiley, London (1968)

    MATH  Google Scholar 

  16. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics, pp. 539–545 (1992)

    Google Scholar 

  17. Heddon, H.: Better living through taxonomies. Digital Web Magazine, http://www.digital-web.com/articles/better_living_through_taxonomies/ (2008)

  18. Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of COLING-ACL98 (1998)

    Google Scholar 

  19. Liu, J., Birnbaum, L.: Measuring semantic similarity between named entities by searching the web directory. In: Web Intelligence, pp. 461–465 (2007)

    Google Scholar 

  20. Liu, J., Birnbaum, L.: What do they think?: Aggregating local views about news events and topics. In: In Proceedings of the 17th International Conference on World Wide Web (WWW 2008), pp. 1021–1022 (2008)

    Google Scholar 

  21. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  22. Moschitti, A.: Efficient convolution kernels for dependency and constituent syntactic trees. In: Proceedings of the 17th European Conference on Machine Learning, Berlin (2006)

    Google Scholar 

  23. Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), pp. 1–69 (2009)

    Article  Google Scholar 

  24. Poesio, M., Ishikawa, T., Schulte im Walde, S., Viera, R.: Acquiring lexical knowledge for anaphora resolution. In: Proceedings of the 3rd Conference on Language Resources and Evaluation (LREC) (2002)

    Google Scholar 

  25. Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of 24th International Conference on Machine Learning, pp. 759–766, June 2007

    Google Scholar 

  26. Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia (2002)

    Google Scholar 

  27. Resnik, P., Lin, J.: Evaluation of NLP systems. In: Clark, A., Fox, C., Lappin, S. (eds.) The Handbook of Computational Linguistics and Natural Language Processing. Wiley-Blackwell, Oxford (2010)

    Google Scholar 

  28. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  29. Schoenmackers, S., Etzioni, O., Weld, D.S.: Scaling textual inference to the web. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2008)

    Google Scholar 

  30. Wang, K., Ming, Z., Chua, T.-S.: A syntactic tree matching approach to finding similar questions in community-based QA services. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’09), pp. 187–194. ACM, New York (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. eBay Inc., San Jose, CA, USA

    Boris A. Galitsky

  2. Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

Authors
  1. Boris A. Galitsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Boris Kovalerchuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boris A. Galitsky .

Editor information

Editors and Affiliations

  1. Department of Higher Mathematics, National Research University Higher School of Economics, Moscow, Russia

    Fuad Aleskerov

  2. Department of Operations, University of Groningen, Groningen, The Netherlands

    Boris Goldengorin

  3. Department of Industrial and Systems Eng, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Galitsky, B.A., Kovalerchuk, B. (2014). Improving Web Search Relevance with Learning Structure of Domain Concepts. In: Aleskerov, F., Goldengorin, B., Pardalos, P. (eds) Clusters, Orders, and Trees: Methods and Applications. Springer Optimization and Its Applications, vol 92. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0742-7_21

Download citation

Publish with us

Policies and ethics

Search

Navigation