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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
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)
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)
Boughanem, M., Brini, A., Dubois, D.: Possibilistic networks for information retrieval. Int. J. Approx. Reason. 50(7), 957–968 (2009)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Salton, G., Fox, E., Wu., H.: Extended Boolean information retrieval. Commun. ACM 26(11), 1022–1036 (1983)
Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. J. Am. Soc. Inf. Sci. JASIS 27(3), 129–146(1976)
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)
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)
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)
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., RÃos, 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
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)
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
Wang, Y., Choi, I., Liu, H.: Generalized ensemble model for document ranking in information retrieval. Comput. Sci. Inf. Syst. 14(1), 123–151 (2016)
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)
Ensan, F., Bagheri, E.: Document retrieval model rough semantic linking. In: Proceedings of WSDM, pp. 181–190. ACM, Cambridge (2017)
SNOMED-CT - SNOMED International. http://www.snomed.org/snomed-ct
Yepes, A.J., Berlanga, R.: Knowledge based word-concept model estimation and refinement for biomedical text mining. J. Biomed. Inform. 53, 300–307 (2015)
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)
Voorhees, E.M.: Using WordNet to disambiguate word senses for text retrieval. In: ACM SIGIR Conference, pp. 171–180. ACM (1993)
Tulkens, S., Å uster, S., Daelemans, W.: Using distributed representations to disambiguate biomedical and clinical concepts. arXiv preprint arXiv: 1608.05605 (2016)
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(4), 267–270 (2004)
Parameswarappa, S., Narayana, V.N.: Kannada word sense disambiguation using decision list. IJETTCS 2(3), 272–278 (2013)
Dayu, Y., Richardson, J., Doherty, R., Evans, C., Altendorf, E.: Semi-supervised Word Sense Disambiguation with Neural Models. ArXiv e-prints (2016)
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)
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
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)
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)
Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft Comput. 17(5), 733–751 (2013)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-05090-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05089-4
Online ISBN: 978-3-030-05090-0
eBook Packages: Computer ScienceComputer Science (R0)