A problem with traditional information retrieval systems is that they typically retrieve information without an explicitly defined domain of interest to the user. Consequently, the system presents a lot of information that is of little relevance to the user. Ideally, the queries’ real intentions should be exposed and reflected in the way the underlying retrieval machinery can deal with them. In this paper we propose using abstraction layers to differentiate on the query terms. We explain why we believe this differentiation of query terms is necessary and the potentials of this approach. The whole retrieval system is under development as part of a Semantic Web standardization project for the Norwegian oil and gas industry.


Information Retrieval Document Collection Latent Semantic Analysis Query Term Query Expansion 
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.
    Gulla, J.A., Auran, P.G., Risvik, K.M.: Linguistic Techniques in Large-Scale Search Engines. Fast Search & Transfer, 15 (2002)Google Scholar
  2. 2.
    Spink, A., Wolfram, D., Jansen, M.B.J., Saracevic, T.: Searching the Web: the public and their queries. J. Am. Soc. Inf. Sci. Technol. 52, 226–234 (2001)CrossRefGoogle Scholar
  3. 3.
    Ozcan, R., Aslangdogan, Y.A.: Concept Based Information Access Using Ontologies and Latent Semantic Analysis. Technical Report CSE-2004-8. University of Texas at Arlington, 16 (2004) Google Scholar
  4. 4.
    Rajapakse, R.K., Denham, M.: Text retrieval with more realistic concept matching and reinforcement learning. Information Processing & Management 42, 1260–1275 (2006)CrossRefGoogle Scholar
  5. 5.
    Grootjen, F.A., van der Weide, T.P.: Conceptual query expansion. Data & Knowledge Engineering 56, 174–193 (2006)CrossRefGoogle Scholar
  6. 6.
    Qiu, Y., Frei, H.-P.: Concept based query expansion. In: Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 160–169. ACM Press, Pittsburgh (1993)CrossRefGoogle Scholar
  7. 7.
    Chang, Y., Ounis, I., Kim, M.: Query reformulation using automatically generated query concepts from a document space. Information Processing and Management 42, 453–468 (2006)CrossRefGoogle Scholar
  8. 8.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  9. 9.
    Tomassen, S.L., Gulla, J.A., Strasunskas, D.: Document Space Adapted Ontology: Application in Query Enrichment. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds.) NLDB 2006. LNCS, vol. 3999, pp. 46–57. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Song, J.-F., Zhang, W.-M., Xiao, W., Li, G.-H., Xu, Z.-N.: Ontology-Based Information Retrieval Model for the Semantic Web. In: Proceedings of EEE 2005, pp. 152–155. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  11. 11.
    Rocha, C., Schwabe, D., de Aragao, M.P.: A hybrid approach for searching in the semantic web. In: Proceeding of WWW 2004, pp. 374–383. ACM, New York (2004)CrossRefGoogle Scholar
  12. 12.
    Ciorăscu, C., Ciorăscu, I., Stoffel, K.: knOWLer - Ontological Support for Information Retrieval Systems. In: Proceedings of Sigir 2003 Conference, Workshop on Semantic Web, Toronto, Canada (2003)Google Scholar
  13. 13.
    Kiryakov, A., Popov, B., Terziev, I., Manov, D., Ognyanoff, D.: Semantic Annotation, Indexing, and Retrieval. Journal of Web Semantics 2(1) (2005)Google Scholar
  14. 14.
    Braga, R.M.M., Werner, C.M.L., Mattoso, M.: Using Ontologies for Domain Information Retrieval. In: Proceedings of the 11th International Workshop on Database and Expert Systems Applications, pp. 836–840. IEEE Computer Society, Los Alamitos (2000)CrossRefGoogle Scholar
  15. 15.
    Borghoff, U.M., Pareschi, R.: Information Technology for Knowledge Management. Journal of Universal Computer Science 3, 835–842 (1997)zbMATHGoogle Scholar
  16. 16.
    Shah, U., Finin, T., Joshi, A., Cost, R.S., Mayfield, J.: Information Retrieval On The Semantic Web. In: Proceedings of Conference on Information and Knowledge Management, pp. 461–468. ACM Press, McLean, Virginia (2002)Google Scholar
  17. 17.
    Vallet, D., Fernández, M., Castells, P.: An Ontology-Based Information Retrieval Model. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 455–470. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Nagypál, G.: Improving Information Retrieval Effectiveness by Using Domain Knowledge Stored in Ontologies. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2005. LNCS, vol. 3762, pp. 780–789. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Paralic, J., Kostial, I.: Ontology-based Information Retrieval. Information and Intelligent Systems, Croatia, 23–28 (2003)Google Scholar
  20. 20.
    Adi, T., Ewell, O.K., Adi, P.: High Selectivity and Accuracy with READWARE’s Automated System of Knowledge Organization. Management Information Technologies, Inc. (MITi) (1999)Google Scholar
  21. 21.
    Chenggang, W., Wenpin, J., Qijia, T., et al.: An information retrieval server based on ontology and multiagent. Journal of computer research & development 38(6), 641–647 (2001)Google Scholar
  22. 22.
    Det Norske Veritas: Tyrihans Terminology for Subsea Equipment and Subsea Production Data. Det Norske Veritas (DNV), p. 60 (2005)Google Scholar
  23. 23.
    Tomassen, S.L.: Research on Ontology-Driven Information Retrieval. In: Meersman, R., Tari, Z., Herrero, P., et al. (eds.) OTM 2006, Springer, Montpellier (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stein L. Tomassen
    • 1
  • Darijus Strasunskas
    • 1
  1. 1.Department of Computer and Information ScienceNorwegian University of Technology and ScienceTrondheimNorway

Personalised recommendations