Construction d'ontologies à partir de textes : la phase de conceptualisation

Dans cet article nous nous interrogeons sur la maniere d'outiller la phase de conceptualisation lors de la construction d'une ontologie a partir de textes. La mise en perspective des resultats obtenus a partir de techniques issues de la terminologie et de la fouille de textes est realisee selon trois plans (discours, linguistique et conceptuel). Cette etude permet de mieux apprehender les moyens envisageables pour outiller efficacement et de facon coherente le processus de conceptualisation.

[1]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[2]  Béatrice Daille,et al.  Variations and application-oriented terminology engineering , 2005 .

[3]  Patrick Pantel,et al.  Concept Discovery from Text , 2002, COLING.

[4]  Richard A. Harshman,et al.  Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..

[5]  Philipp Cimiano,et al.  Ontology Learning from Text: Methods, Evaluation and Applications , 2005 .

[6]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[7]  Nathalie Aussenac-Gilles,et al.  The TERMINAE Method and Platform for Ontology Engineering from Texts , 2008, Ontology Learning and Population.

[8]  Thierry Hamon,et al.  A Step towards the Detection of Semantic Variants of Terms in Technical Documents , 1998, COLING-ACL.

[9]  Johanna Völker,et al.  A Framework for Ontology Learning and Data-driven Change Discovery , 2005 .

[10]  Yimin Wang,et al.  Towards Semi-automatic Ontology Building Supported by Large-Scale Knowledge Acquisition , 2006, AAAI Fall Symposium: Semantic Web for Collaborative Knowledge Acquisition.

[11]  Christian Jacquemin,et al.  Term Extraction and Automatic Indexing , 2005 .

[12]  Z. Harris,et al.  Book Reviews: The Form of Information in Science: Analysis of an Immunology Sublanguage , 1989, CL.

[13]  Adeline Nazarenko,et al.  Une mesure de pertinence pour le tri de l’information dans un index de “fin de livre” , 2004, JEPTALNRECITAL.

[14]  David Faure,et al.  Knowledge Acquisition of Predicate Argument Structures from Technical Texts Using Machine Learning: The System ASIUM , 1999, EKAW.

[15]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[16]  Thierry Hamon,et al.  Improving Term Extraction with Terminological Resources , 2006, FinTAL.

[17]  Nathalie Aussenac-Gilles,et al.  Modélisation du domaine par une méthode fondée sur l'analyse de corpus , 2000 .

[18]  D. Mladení,et al.  SEMI-AUTOMATIC DATA-DRIVEN ONTOLOGY CONSTRUCTION SYSTEM , 2006 .

[19]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[20]  Nicolas Béchet.  Utilisation d'informations syntaxico-sémantiques associées à LSA , 2007 .

[21]  Zellig S. Harris,et al.  The form of information in science , 1988 .

[22]  Daphne Koller,et al.  Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.

[23]  Steffen Staab,et al.  Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis , 2005, J. Artif. Intell. Res..

[24]  Patrick Drouin.  Termhood experiments: quantifying the relevance of candidate terms , 2006 .

[25]  Philipp Cimiano,et al.  Ontology learning and population from text - algorithms, evaluation and applications , 2006 .