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Genre/Form: | Thèses et écrits académiques |
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Additional Physical Format: | Vers une capitalisation des connaissances orientée utilisateur : extraction et structuration automatiques de l'information issue de sources ouvertes / Laurie Serrano Villeurbanne : [CCSD], 2014 (ABES)226676684 Vers une capitalisation des connaissances orientée utilisateur : extraction et structuration automatiques de l'information issue de sources ouvertes / Laurie Serrano Lille : Atelier national de reproduction des thèses, 2014 Microfiches. (@Lille-Thèses) (ABES)248065246 |
Material Type: | Thesis/dissertation |
Document Type: | Book |
All Authors / Contributors: |
Laurie Serrano; Maroua Bouzid, chercheuse en informatique).; Thierry Charnois; Gaël Harry Dias; Laurence Cholvy; Thierry Poibeau; Stéphan Brunessaux; Fatiha Saïs; Université de Caen Normandie.; EADS (France).; École doctorale structures, informations, matière et matériaux (Caen / 1992-2016).; Groupe de recherche en informatique, image, automatique et instrumentation de Caen (1995-....). |
OCLC Number: | 907124120 |
Description: | 1 vol. (XII-188 p.) ; 30 cm. |
Responsibility: | Laurie Serrano ; [sous la direction de] Marua Bouzid et Thierry Charnois et Stéphan Brunessaux. |
Abstract:
Due to the considerable increase of freely available data (especially on the Web), the discovery of relevant information from textual content is a critical challenge. Open Source Intelligence (OSINT) specialists are particularly concerned by this phenomenon as they try to mine large amounts of heterogeneous information to acquire actionable intelligence. This collection process is still largely done by hand in order to build knowledge sheets summarizing all the knowledge acquired about a specific entity. Given this context, the main goal of this thesis work is to reduce and facilitate the daily work of intelligence analysts. For this sake, our researches revolve around three main axis: knowledge modeling, text mining and knowledge gathering. We explored the literature related to these different domains to develop a global knowledge gathering system. Our first contribution is the building of a domain ontology dedicated to knowledge representation for OSINT purposes and that comprises a specific definition and modeling of the event concept for this domain. Secondly, we have developed and evaluated an event recognition system which is based on two different extraction approaches: the first one is based on hand-crafted rules and the second one on a frequent pattern learning technique. As our third contribution, we proposed a semantic aggregation process as a necessary post-processing step to enhance the quality of the events extracted and to convert extraction results into actionable knowledge. This is achieved by means of multiple similarity measures between events, expressed according a qualitative scale which has been designed following our final users' needs."""""
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