How Ontology Based Information Retrieval Systems May Benefit from Lexical Text Analysis

Autor: Jacky Montmain, Benjamin Duthil, Vincent Ranwez, Patrick Augereau, Mohameth François Sy, Sylvie Ranwez
Přispěvatelé: Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut de recherche en cancérologie de Montpellier (IRCM - U896 Inserm - UM1), CRLCC Val d'Aurelle - Paul Lamarque-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Montpellier 1 (UM1), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Montpellier 1 (UM1)-CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ITMO Cancer, Oltramari, Alessandro, Vossen, Piek, Qin, Lu, Hovy, Eduard, Knowledge and Image analysis for Decision making (KID), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-IMT - MINES ALES (IMT - MINES ALES)
Rok vydání: 2012
Předmět:
Zdroj: New Trends of Research in Ontologies and Lexical Resources ISBN: 9783642317811
New Trends of Research in Ontologies and Lexical Resources
New Trends of Research in Ontologies and Lexical Resources, 15, Springer, 282 p, 2013, Theory and Applications of Natural Language Processing, 978-3-642-31782-8
Oltramari, Alessandro; Vossen, Piek; Qin, Lu; Hovy, Eduard. New Trends of Research in Ontologies and Lexical Resources, 15, Springer, pp.209-230, 2013, Theory and Applications of Natural Language Processing, 978-3-642-31781-1
HAL
Jacky Montmain
Oltramari, Alessandro; Vossen, Piek; Qin, Lu; Hovy, Eduard. New Trends of Research in Ontologies and Lexical Resources, Springer, pp.209-230, 2013, Theory and Applications of Natural Language Processing, 978-3-642-31781-1
Popis: The exponential growth of available electronic data is almost useless without efficient tools to retrieve the right information at the right time. This is especially crucial in the context of decision making (e.g. for politicians), innovative development (e.g. for scientists and industrials) or economic development (e.g. for market or concurrence studies). It is now widely acknowledged that information retrieval systems (IRS in short) need to take semantics into account. In this context, semantic Web technologies have been rapidly widespread and accepted. This article surveys semantic based methodologies designed to efficiently retrieve and exploit information. Some of them, based on terminologies, are fitted to open context, dealing with heterogeneous and unstructured data, while others, based on taxonomies or ontologies, are semantically richer but require formal knowledge representation of the studied domain. Hence, a continuum of solutions exists from terminology to ontology based IRSs. These approaches are often seen as concurrent and exclusive, but this chapter asserts that their advantages may be efficiently combined in a hybrid solution built upon domain ontology. The original approach presented here benefits from both lexical and ontological document description, and combines them in a software architecture dedicated to information retrieval in specific domains. Relevant documents are first identified via their conceptual indexing based on domain ontology, and then each document is segmented to highlight text fragments that deal with users’ information needs.The system thus specifies why these documents have been chosen and facilitates end-user information gathering.
Databáze: OpenAIRE