Using Semantic Knowledge to Improve Web Query Processing

  • Jordi Conesa
  • Veda C. Storey
  • Vijayan Sugumaran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3999)


Although search engines are very useful for obtaining information from the World Wide Web, users still have problems obtaining the most relevant information when processing their web queries. Prior research has attempted to use different types of knowledge to improve web querying processing. This research presents a methodology for employing a specific body of knowledge, ResearchCyc, which provides semantic knowledge about different application domains. Semantic knowledge from ResearchCyc, as well as linguistic knowledge from WordNet, is employed. An analysis of different queries from different application domains using the semantic and linguistic knowledge illustrates how more relevant results can be obtained.


Query Term Query Expansion Semantic Knowledge Word Sense Linguistic Knowledge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McGuinness, D.: Question Answering on the Semantic Web. IEEE Intelligent Systems 19(1), 82–85 (2004)CrossRefGoogle Scholar
  2. 2.
    Fellbaum, V.: Introduction. In: Introduction: Wordnet: An Electronic Lexical Database. MIT Press, Cambridge (1998)Google Scholar
  3. 3.
    Cyc. Cyc Ontology, available from:
  4. 4.
    Liu, H., Singh, P.: ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal 22 (2004)Google Scholar
  5. 5.
    Burton-Jones, A., Storey, V.C., Sugumaran, V., Purao, S.: A Heuristic-Based Methodology for Semantic Augmentation of User Queries on the Web. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003. LNCS, vol. 2813, pp. 476–489. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Hartmann, J., et al.: Ontology-Based Query Refinement for Semantic Portals. In: Integrated Publication and Information Systems to Virtual Information and Knowledge Environments 2005, pp. 41–50 (2005)Google Scholar
  7. 7.
    Khan, M.S., Khor, S.: Enhanced web document retrieval using automatic query expansion. Jl. of the American Soc for Info. Science and Technology 55(1), 29–40 (2004)CrossRefGoogle Scholar
  8. 8.
    Storey, V.C., Sugumaran, V., Burton-Jones, A.: The Role of User Profiles in Context-Aware Query Processing for the Semantic Web. In: Meziane, F., Métais, E. (eds.) NLDB 2004. LNCS, vol. 3136, pp. 51–63. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Stojanovic, N., Studer, R., Stojanovic, L.: An Approach for Step-By-Step Query Refinement in the Ontology-based Information Retrieval. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, Beijing, China (2004)Google Scholar
  10. 10.
    Zhang, L., Ram, S., Zeng, D.D.: Contextual Web Search Based on Semantic Relationships. In: Proceedings of the 15th Workshop on Information Technology and Systems (WITS 2005). Las Vegas, Nevada (2005)Google Scholar
  11. 11.
    Grootjen, F.A., Weide, T.P.v.d.: Conceptual query expansion. Data & Knowledge Engineering 56(2), 174–193 (2006)CrossRefGoogle Scholar
  12. 12.
    Mandala, R., Tokunaga, T., Tanaka, H.: Combining multiple evidence from different types of thesaurus for query expansion. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, pp. 191–197 (1999)Google Scholar
  13. 13.
    Masters, J.: Structured Knowledge Source Integration and its applications to information fusion. In: Fifth International Conference on Information Fusion, Annapolis (2002)Google Scholar
  14. 14.
    Matuszek, C., et al.: Searching for common sense: populating Cyc from the web. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (2005)Google Scholar
  15. 15.
    O’Hara, T., et al.: Inducing criteria for mass noun lexical mappings using the Cyc KB, and its extension to WordNet. In: Workshop on Computational Semantics, Tilburg (2003)Google Scholar
  16. 16.
    Vélez, B., et al.: Fast and Effective Query Refinement. In: Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 6–15 (1997)Google Scholar
  17. 17.
    Uschold, M.: Knowledge level modelling: concepts and terminology. The Knowledge Engineering Review 13(1), 5–29 (1998)CrossRefGoogle Scholar
  18. 18.
    Mason, O.: QTag - a Portable POS Tagger (2005) (accessed January 7, 2005), available from:
  19. 19.
    Embley, D.W.: Toward semantic understanding—an approach based on information extraction ontologies. In: 15th Australasian Database Conference, New Zealand, pp. 3–12 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jordi Conesa
    • 1
  • Veda C. Storey
    • 2
  • Vijayan Sugumaran
    • 3
  1. 1.Departament de llenguatges i sistemas informàticsUniversitat Politecnica de CatalunyaBarcelonaSpain
  2. 2.Department of Computer Information Systems, J. Mark Robinson Collage of BusinessGeorgia State UniversityAtlantaUSA
  3. 3.School of Business AdministrationOakland UniversityRochesterUSA

Personalised recommendations