A Simple WordNet-Ontology Based Email Retrieval System for Digital Forensics

  • Phan Thien Son
  • Lan Du
  • Huidong Jin
  • Olivier de Vel
  • Nianjun Liu
  • Terry Caelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5075)

Abstract

Because of the high impact of high-tech digital crime upon our society, it is necessary to develop effective Information Retrieval (IR) tools to support digital forensic investigations. In this paper, we propose an IR system for digital forensics that targets emails. Our system incorporates WordNet (i.e. a domain independent ontology for the vocabulary) into an Extended Boolean Model (EBM) by applying query expansion techniques. Structured Boolean queries in Backus-Naur Form (BNF) are utilized to assist investigators in effectively expressing their information requirements. We compare the performance of our system on several email datasets with a traditional Boolean IR system built upon the Lucene keyword-only model. Experimental results show that our system yields a promising improvement in retrieval performance without the requirement of very accurate query keywords to retrieve the most relevant emails.

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References

  1. 1.
    Casey, E.: Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet with CDROM. Academic Press, Inc., London (2000)Google Scholar
  2. 2.
    de Vel, O.Y., Liu, N., Caelli, T., Caetano, T.S.: An embedded bayesian network hidden markov model for digital forensics. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, F.-Y. (eds.) ISI 2006. LNCS, vol. 3975, pp. 459–465. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Salton, et al.: Extended boolean information retrieval. Commun. ACM 26(11), 1022–1036 (1983)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-hill, New York (1983)MATHGoogle Scholar
  5. 5.
    Wong, et al.: Generalized vector spaces model in information retrieval. In: SIGIR 1985, pp. 18–25. ACM Press, New York (1985)Google Scholar
  6. 6.
    Voorhees, E.M.: Query expansion using lexical-semantic relations. In: SIGIR 1994, pp. 61–69 (1994)Google Scholar
  7. 7.
    Parapar, et al.: Query expansion using WordNet with a logical model of information retrieval. In: IADIS AC, pp. 487–494 (2005)Google Scholar
  8. 8.
    Mandala, et al.: The use of WordNet in information retrieval. In: Proceedings of Use of WordNet in Natural Language Processing Systems, pp. 31–37 (1998)Google Scholar
  9. 9.
    Grootjen, F.A., van der Weide, T.P.: Conceptual query expansion. Data Knowl. Eng. 56(2), 174–193 (2006)CrossRefGoogle Scholar
  10. 10.
    Moldovan, D.I., Mihalcea, R.: Using WordNet and lexical operators to improve internet searches. IEEE Internet Computing 4(1), 34–43 (2000)CrossRefGoogle Scholar
  11. 11.
    Finkelstein, et al.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20(1), 116–131 (2002)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Zukerman, et al.: Query expansion and query reduction in document retrieval. In: ICTAI 2003 (2003)Google Scholar
  13. 13.
    Liu, et al.: An effective approach to document retrieval via utilizing WordNet and recognizing phrases. In: SIGIR 2004, pp. 266–272 (2004)Google Scholar
  14. 14.
    Gong, et al.: Web query expansion by WordNet. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 166–175. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  16. 16.
    Wirth, N.: What can we do about the unnecessary diversity of notation for syntactic definitions? Commun. ACM 20(11), 822–823 (1977)CrossRefGoogle Scholar
  17. 17.
    Widdows, D.: Orthogonal negation in vector spaces for modelling word-meanings and document retrieval. In: Dignum, F.P.M. (ed.) ACL 2003. LNCS (LNAI), vol. 2922, pp. 136–143. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Krovetz, R., Croft, W.B.: Lexical ambiguity and information retrieval. ACM Trans. Inf. Syst. 10(2), 115–141 (1992)CrossRefGoogle Scholar
  19. 19.
    Liu, et al.: Word sense disambiguation in queries. In: CIKM 2005, pp. 525–532 (2005)Google Scholar
  20. 20.
    Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Phan Thien Son
    • 1
    • 2
  • Lan Du
    • 1
    • 2
  • Huidong Jin
    • 1
    • 2
  • Olivier de Vel
    • 3
  • Nianjun Liu
    • 1
    • 2
  • Terry Caelli
    • 1
    • 2
  1. 1.NICTA Canberra LabCanberraAustralia
  2. 2.RSISEthe Australian National UniversityCanberraAustralia
  3. 3.Command, Control, Communications and Intelligence DivisionDSTOEdinburghAustralia

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