ONTOLOGY-BASED ADAPTIVE QUERY REFINEMENT

Lefteris Kozanidis, Paraskevi Tzekou, Nikos Zotos, Sofia Stamou, Dimitris Christodoulakis

2007

Abstract

Query refinement is the process of providing Web information seekers with alternative wordings for expressing their information needs. Although alternative query formulations may contribute to the improvement of retrieval results, nevertheless their realization by Web users is intrinsically limited in that alternative query wordings convey explicit information about neither their degree nor their type of correlation to the user-issued queries. Moreover, alternative query formulations are determined based on the semantics of the issued query alone and they do not consider anything about the search intentions of the user issuing that query. In this paper, we introduce a novel query refinement technique which uses a lexical ontology for identifying alternative query formulations that are both informative of the user’s interests and related to the user selected queries. The most innovative feature of our technique is the visualization of the alternative query wordings in a graphical representation form, which conveys explicit information about the refined queries correlation to the user issued requests and which allows the user select which terms to participate in the refinement process. Experimental results demonstrate that our method has a significant potential in improving the user search experience.

References

  1. Billerbeck, B., Scholer, F., Williams, H.E., Zobel, J., 2003. Query Expansion Using Associated Queries. In Proceedings of the ACM CIKM International Conference on Information and Knowledge Management, New Orleans, Louisiana, USA.
  2. Celik, D., Elci, A. 2006. Discovering and Scoring of Semantic Web Services based on Client Requirement(s) through a Semantic Search Agent. In Proceedings of the 30th Annual International Computer Software and Applications Conference, Vol. II, IEEE Computer Society Press, pp. 273-278.
  3. Chen, H., Schatz, B., Yim, T., Fye, D., 1995. Automatic Thesaurus Generation for an Electronic Community System. In ASIS Journal, Vol. 46(3) pp. 175-193.
  4. Crouch, C., 1990. An Approach to the Automatic Constructions of Global Thesauri. In Information Processing and Management, Vol. 26(5) pp. 139-147.
  5. Fellbaum, Ch., 1998. WordNet: an Electronic Lexical Database, MIT Press.
  6. Fitzpatrick, L., Dent, M., 1997. Automatic Feedback Using Past Queries: Social Searching? In Proceedings of the 20th ACM-SIGIR Conference, pp. 306-313.
  7. Gliozzo, A., Strapparava, C., Dagan, I., 2004. Unsupervised and Supervised Exploitation of Semantic Domains in Lexical Disambiguation. In Computer Speech and Language, 18(3) pp. 275-299.
  8. Grefenstette, G., 1992. Use of Syntactic Context to Produce Term Association Lists for Text Retrieval. In Proceedings of the 15th ACM SIGIR Conference.
  9. Harman, D., 1992. Relevance Feedback Revisited. In Proceedings of the 15th ACM SIGIR Conference.
  10. Hawking, D., Craswell, N., 2001. Overview of the TREC2001 Web Track. In: Voorhees, E., Harman, D.K. (eds.): The Tenth Retrieval Conference. NIST Special Publication pp. 500-250, Washington D.C.
  11. Jing, Y., Croft, B., 1994. An Association Thesaurus for Information Retrieval. In RIAO Conference.
  12. Khan, L., McLeod, D., Hovy, E., 2004. Retrieval Effectiveness of an Ontology-Based Model for Information Selection. In VLDB Journal, Vol. (13) pp. 71-85.
  13. Qui, Y., Frei, H.P., 1993. Concept Based Query Expansion. In Proceedings of the 16th ACM SIGIR Conference.
  14. Resnik, Ph., 2005. Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In Proceedings of the 14th Intl. Joint Conference on Artificial Intelligence, pp. 448-453.
  15. Salton, G., Buckley, C., 1998. Term Weighting Approaches in Automatic Text Retrieval. In Information Processing and Management, Vol. 24(5) pp. 513-523.
  16. Smeaton, A.F., van Rijsbergen, C.J., 1993. The Retrieval Effects on Query Expansion on a Feedback Document Retrieval System. In Computer Journal, Vol. 26(3) pp. 239-246.
  17. Spark Jones, K., Barber, E.B., 1971. What Makes an Automatic Keyword Classification. In ASIS Journal, Vol. (18) pp. 166-175.
  18. Vossen, P., 1998. EuroWordNet: a Multilingual Database with Lexical Semantic Networks. Kluwer Academic Publishers.
  19. Xu, J., Croft, B., 1996. Query Expansion Using Local and Global Document Analysis. In Proceedings of the 15th ACM SIGIR Conference
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Paper Citation


in Harvard Style

Kozanidis L., Tzekou P., Zotos N., Stamou S. and Christodoulakis D. (2007). ONTOLOGY-BASED ADAPTIVE QUERY REFINEMENT . In Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-972-8865-78-8, pages 43-50. DOI: 10.5220/0001267300430050


in Bibtex Style

@conference{webist07,
author={Lefteris Kozanidis and Paraskevi Tzekou and Nikos Zotos and Sofia Stamou and Dimitris Christodoulakis},
title={ONTOLOGY-BASED ADAPTIVE QUERY REFINEMENT},
booktitle={Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2007},
pages={43-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001267300430050},
isbn={978-972-8865-78-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - ONTOLOGY-BASED ADAPTIVE QUERY REFINEMENT
SN - 978-972-8865-78-8
AU - Kozanidis L.
AU - Tzekou P.
AU - Zotos N.
AU - Stamou S.
AU - Christodoulakis D.
PY - 2007
SP - 43
EP - 50
DO - 10.5220/0001267300430050