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Possibilistic Information Retrieval Model Based on a Multi-terminology

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Book cover Advanced Data Mining and Applications (ADMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11323))

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Abstract

We proposed in this paper a new approach for information retrieval intitled Conceptual Information Retrieval Model (CIRM). Our contribution is to exploit possibilistic networks (PN) and a multi-terminology in order to extract and disambiguate terms and then to retrieve documents. The two measures of possibility and necessity were used to select the relevant concept of an ambiguous term. Thus, the user query and unstructured documents are described throught a conceptual representation. Concepts were then filtered and ranked. Finally, a possibilistic network was exploited to match documents and queries. Two biomedical terminologies were exploited which are the MeSH thesaurus (Medical Subject Headings) and the SNOMED-CT ontology (Systematized Nomenclature of Medicine of Clinical Terms). The experimentations performed with CIRM on the OHSUMED corpus showed encouraging results: the improvement rates are +43.18% and +43.75% in terms of Main Average Precision and Normalized Discounted Cumulative Gain when compared to the baseline.

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References

  1. Nelson, S.J., Johnson, W.D., Humphreys, B.L.: Relationships in medical subject heading. In: Relationships in the Organization of Knowledge, pp. 171–184 (2001)

    Google Scholar 

  2. Chebil, W., Soualmia, L.F., Omri, M.N., Darmoni, S.J.: Indexing biomedical documents with a possibilistic network. J. Assoc. Inf. Sci. Technol. 67(4), 928–941 (2016)

    Article  Google Scholar 

  3. Boughanem, M., Brini, A., Dubois, D.: Possibilistic networks for information retrieval. Int. J. Approx. Reason. 50(7), 957–968 (2009)

    Article  MathSciNet  Google Scholar 

  4. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  Google Scholar 

  5. Salton, G., Fox, E., Wu., H.: Extended Boolean information retrieval. Commun. ACM 26(11), 1022–1036 (1983)

    Article  MathSciNet  Google Scholar 

  6. Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. J. Am. Soc. Inf. Sci. JASIS 27(3), 129–146(1976)

    Article  Google Scholar 

  7. Robertson, S., Walker, S.: Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In: Proceedings of the International ACM-SIGIR Conference, pp. 232–241. ACM (1994)

    Google Scholar 

  8. Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR, pp. 275–281. ACM, Melbourne (1998)

    Google Scholar 

  9. Turtle, H., Croft, W.B.: Inference networks for document retrieval. In: ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–24. ACM, Brussels (1990)

    Google Scholar 

  10. Majdoubi, J., Tmar, M., Gargouri, F.: Using the MeSH thesaurus to index a medical article: combination of content, structure and semantics. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009. LNCS, vol. 5711, pp. 277–84. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04595-0_34

    Chapter  Google Scholar 

  11. Dinh, D., Tamine, L.: Towards a context sensitive approach to searching information based on domain specific knowledge sources. J. Web Sem. 12, 41–52 (2012)

    Article  Google Scholar 

  12. Prasath, R., Sarkar, S., O’Reilly, P.: Improving cross language information retrieval using corpus based query suggestion approach. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 448–457. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_33

    Chapter  Google Scholar 

  13. Wang, Y., Choi, I., Liu, H.: Generalized ensemble model for document ranking in information retrieval. Comput. Sci. Inf. Syst. 14(1), 123–151 (2016)

    Article  Google Scholar 

  14. Sneiders, E.: Text retrieval by term co-occurrences in a query based vector space. In: Proceedings of COLING the 26th International Conference on Computational Linguistics, Osaka, Japan, pp. 2356–2365 (2016)

    Google Scholar 

  15. Ensan, F., Bagheri, E.: Document retrieval model rough semantic linking. In: Proceedings of WSDM, pp. 181–190. ACM, Cambridge (2017)

    Google Scholar 

  16. SNOMED-CT - SNOMED International. http://www.snomed.org/snomed-ct

  17. Yepes, A.J., Berlanga, R.: Knowledge based word-concept model estimation and refinement for biomedical text mining. J. Biomed. Inform. 53, 300–307 (2015)

    Article  Google Scholar 

  18. Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the Annual International Conference on Systems Documentation, pp. 24–26. ACM, Toronto (1986)

    Google Scholar 

  19. Voorhees, E.M.: Using WordNet to disambiguate word senses for text retrieval. In: ACM SIGIR Conference, pp. 171–180. ACM (1993)

    Google Scholar 

  20. Tulkens, S., Šuster, S., Daelemans, W.: Using distributed representations to disambiguate biomedical and clinical concepts. arXiv preprint arXiv: 1608.05605 (2016)

  21. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(4), 267–270 (2004)

    Article  Google Scholar 

  22. Parameswarappa, S., Narayana, V.N.: Kannada word sense disambiguation using decision list. IJETTCS 2(3), 272–278 (2013)

    Google Scholar 

  23. Dayu, Y., Richardson, J., Doherty, R., Evans, C., Altendorf, E.: Semi-supervised Word Sense Disambiguation with Neural Models. ArXiv e-prints (2016)

    Google Scholar 

  24. Panchenko, A., Ruppert, E., Faralli, S.: Unsupervised does not mean uninterpretable: the case for word sense induction and disambiguation. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp. 86–98 (2017)

    Google Scholar 

  25. Chebil, W., Soualmia, L.F., Darmoni, S.J.: BioDI: a new approach to improve biomedical documents indexing. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013. LNCS, vol. 8055, pp. 78–87. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40285-2_9

    Chapter  Google Scholar 

  26. Chebil, W., Soualmia, L.F., Omri, M.N., Darmoni, S.J.: Extraction possibiliste de concepts MeSH a partir de documents biomedicaux. Revue d Intell. Artificielle 28(6), 729–752 (2014)

    Article  Google Scholar 

  27. Chebil, W., Soualmia, L., Omri, M.N., Darmoni, S.J.: Biomedical documents indexing with Bayesian networks and terminologies. In: 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–6. IEEE, Nanjing (2017)

    Google Scholar 

  28. Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft Comput. 17(5), 733–751 (2013)

    Article  Google Scholar 

  29. De Campos, L.M., Fernandez-Luna, J.M., Huete, J.F.: The BNR model : foundations and performance of Bayesian network-based retrieval model. Int. J. Approximate Reasoning 34, 265–285 (2003)

    Google Scholar 

  30. Hersh, W., Buckley, C., Leone, T.J., Hickam, D.: OHSUMED: an interactive retrieval evaluation and new large test collection for research. In: Croft, B.W. (ed.) SIGIR 1994, pp. 192–201. ACM/Springer, Dublin (1994)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. MARS Research Laboratory, University of Sousse, Sousse, Tunisia

    Wiem Chebil & Mohamed Nazih Omri

  2. LITIS-TIBS EA 4108, Normandie University, Rouen University, Rouen, France

    Lina F. Soualmia

Authors
  1. Wiem Chebil
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  2. Lina F. Soualmia
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  3. Mohamed Nazih Omri
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Corresponding author

Correspondence to Wiem Chebil .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Guojun Gan

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  3. The University of Queensland, Brisbane, QLD, Australia

    Xue Li

  4. Beijing Institute of Technology, Beijing, China

    Shuliang Wang

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Chebil, W., Soualmia, L.F., Omri, M.N. (2018). Possibilistic Information Retrieval Model Based on a Multi-terminology. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-05090-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05089-4

  • Online ISBN: 978-3-030-05090-0

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