Biomedical Concepts Extraction Based on Possibilistic Network and Vector Space Model

  • Wiem ChebilEmail author
  • Lina Fatima Soualmia
  • Mohamed Nazih Omri
  • Stéfan Jacques Darmoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9105)


This paper proposes a new approach for indexing biomedical documents based on the combination of a Possibilistic Network and a Vector Space Model. This later carries out partial matching between documents and biomedical vocabularies. The main contribution of the proposed approach is to combine the cosine similarity and the two measures of possibility and necessity to enhance the estimation of the similarity between a document and a given concept. The possibility estimates the extent to which a document is not similar to the concept. The necessity allows the confirmation that the document is similar to the concept. Experiments were carried out on the OSHUMED corpora and showed encouraging results.


Indexing Biomedical documents Possibilistic network Vector space model Partial matching 


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  1. 1.
    Singhal, A.: Modern information retrieval: A brief overview. IEEE Data Engineering Bulletin 24(4), 35–43 (2009)Google Scholar
  2. 2.
    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
  3. 3.
    Ruch, P.: Automatic assignment of biomedical categories: towards a generic approach. Bioinformatics Journal 22(6), 658–664 (2006)CrossRefGoogle Scholar
  4. 4.
    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, Part I. LNCS, vol. 8055, pp. 78–87. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Chebil, W., Soualmia, L.F., Omri, M.N., Darmoni, S.J.: Indexing biomedical documents with a possibilistic network. Journal of the Association for Information Science and Technology (in press, 2015), doi: 10.1002/asi.23435Google Scholar
  6. 6.
    Dubois, D., Prade, H.: Possibility Theory. Plenum (1988)Google Scholar
  7. 7.
    Omri, M.N., Chouigui, N.: Measure of similarity between fuzzy concepts for identification of fuzzy user’s requests in fuzzy semantic networks. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 9(6), 743–748 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Boughanem, M., Brini, A., Dubois, D.: Possibilistic networks for information retrieval. International Journal of Approximate Reasoning 50, 957–968 (2009)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Chebil, W., Soualmia, L.F., Dahamna, B., Darmoni, S.J.: Indexation automatique de do-cuments en santé: évaluation et analyse de sources d’erreurs. BioMedical Engineering and Research 33(5-6), 129–136 (2012)Google Scholar
  10. 10.
    Dinh, D., Tamine, L.: Towards a context sensitive approach to searching information based on domain specific knowledge sources. Web Semantics: Science, Services and Agents on the World Wide Web 12-13, 41–52 (2012)Google Scholar
  11. 11.
    Hliaoutakis, A., Zervanou, K., Petrakis, E.G.M.: The AMTEx approach in the medical document indexing and retrieval application. Data Knowledge Engineering 68(3), 380–392 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Wiem Chebil
    • 1
    • 2
    Email author
  • Lina Fatima Soualmia
    • 1
    • 3
  • Mohamed Nazih Omri
    • 2
  • Stéfan Jacques Darmoni
    • 1
    • 3
  1. 1.LITIS-TIBS EA 4108Normandie University, Rouen University and HospitalRouenFrance
  2. 2.Research unit MARSMonastir UniversityMonastirTunisia
  3. 3.French National Institute for Health, INSERM, LIMICS UMR 1142ParisFrance

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