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Concept Search

  • Fausto Giunchiglia
  • Uladzimir Kharkevich
  • Ilya Zaihrayeu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)

Abstract

In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics.

Keywords

Noun Phrase Complex Concept Mean Average Precision Word Sense Information Retrieval System 
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.

References

  1. 1.
    Andreasen, T., Anker Jensen, P., Fischer Nilsson, J., Paggio, P., Pedersen, B.S., Thomsen, H.E.: Content-based text querying with ontological descriptors. Data & Know. Eng. 48(2), 199–219 (2004)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bhagdev, R., Chapman, S., Ciravegna, F., Lanfranchi, V., Petrelli, D.: Hybrid search: Effectively combining keywords and semantic searches. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 554–568. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Celino, I., Della Valle, E., Cerizza, D., Turati, A.: Squiggle: a semantic search engine for indexing and retrieval of multimedia content. In: SEMPS (2006)Google Scholar
  4. 4.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A framework and graphical development environment for robust NLP tools and applications. In: 40th Anniversary Meeting of the Association for Computational Linguistics (2002)Google Scholar
  5. 5.
    Davies, J., Weeks, R.: QuizRDF: Search technology for the semantic web. In: HICSS (2004)Google Scholar
  6. 6.
    Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  7. 7.
    Giunchiglia, F., Kharkevich, U., Zaihrayeu, I.: Concept search: Semantics enabled syntactic search. In: SemSearch 2008 workshop at ESWC (2008)Google Scholar
  8. 8.
    Giunchiglia, F., Shvaiko, P., Yatskevich, M.: Discovering missing background knowledge in ontology matching. In: Proc. of ECAI (2006)Google Scholar
  9. 9.
    Giunchiglia, F., Yatskevich, M., Shvaiko, P.: Semantic matching: Algorithms and implementation. Journal on Data Semantics (JoDS) 9, 1–38 (2007)zbMATHGoogle Scholar
  10. 10.
    Hildebrand, M., van Ossenbruggen, J., Hardman, L.: An analysis of search-based user interaction on the semantic web. Technical Report INS-E0706, CWI (2007)Google Scholar
  11. 11.
    Magnini, B., Speranza, M., Girardi, C.: A semantic-based approach to interoperability of classification hierarchies: evaluation of linguistic techniques. In: COLING 2004 (2004)Google Scholar
  12. 12.
    Mangold, C.: A survey and classification of semantic search approaches. Int. J. Metadata Semantics and Ontology 2(1), 23–34 (2007)CrossRefGoogle Scholar
  13. 13.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  14. 14.
    Moldovan, D.I., Mihalcea, R.: Using wordnet and lexical operators to improve internet searches. IEEE Internet Computing 4(1), 34–43 (2000)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Rocha, C., Schwabe, D., de Aragao, M.: A hybrid approach for searching in the semantic web. In: 13th International World Wide Web Conference (2004)Google Scholar
  17. 17.
    Sanderson, M.: Retrieving with good sense. Inf. Retr. 2(1), 49–69 (2000)CrossRefGoogle Scholar
  18. 18.
    Schutze, H., Pedersen, J.O.: Information retrieval based on word senses. In: 4th Annual Symposium on Document Analysis and Information Retrieval (1995)Google Scholar
  19. 19.
    Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)zbMATHGoogle Scholar
  20. 20.
    Stokoe, C., Oakes, M.P., Tait, J.: Word sense disambiguation in information retrieval revisited, pp. 159–166 (2003)Google Scholar
  21. 21.
    Woods, W.A.: Conceptual indexing: A better way to organize knowledge. Technical Report TR-97-61, Sun Microsystems Laboratories, USA (1997)Google Scholar
  22. 22.
    Zaihrayeu, I., Sun, L., Giunchiglia, F., Pan, W., Ju, Q., Chi, M., Huang, X.: From web directories to ontologies: Natural language processing challenges. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 623–636. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Zhai, C.: Fast statistical parsing of noun phrases for document indexing. In: Fifth Conference on Applied Natural Language Processing, pp. 312–319 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fausto Giunchiglia
    • 1
  • Uladzimir Kharkevich
    • 1
  • Ilya Zaihrayeu
    • 1
  1. 1.Department of Information Engineering and Computer ScienceUniversity of TrentoItaly

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