Possibilistic Information Retrieval Model Based on a Multi-terminology
| Autor: | Mohamed Nazih Omri, Wiem Chebil, Lina Fatima Soualmia |
|---|---|
| Přispěvatelé: | Université de Sousse, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Traitement de l'information en Biologie Santé (TIBS - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Thesaurus (information retrieval)
Information retrieval Exploit Computer science concepts InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Subject (documents) 02 engineering and technology Possibilistic networks Ontology (information science) terms disambiguation 3. Good health Terminology Term (time) Systematized Nomenclature of Medicine 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] information retrieval Representation (mathematics) biomedical terminologies ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Advanced Data Mining and Applications-14th International Conference Advanced Data Mining and Applications-14th International Conference, Nov 2018, Nanjing, China. pp.66-79, ⟨10.1007/978-3-030-05090-0_6⟩ Advanced Data Mining and Applications ISBN: 9783030050894 ADMA |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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