LIP6 2000/005
- Thesis
Construction et Exploitation de Réseaux Sémantiques Flous pour l'Extraction d'Information Pertinente : Le système RELIEFS - Ch. Brouard
- 213 pages - 01/07/2000- document en - http://www.lip6.fr/lip6/reports/2000/lip6.2000.005.ps.gz - 698 Ko
- Contact : brouardc (at) nullIRO.UMontreal.CA
- Ancien Thème : APA
- Keywords : Relevance, semantic networks, fuzzy sets, data-mining
- Publisher : Valerie.Mangin (at) nulllip6.fr
Relevance is a central notion for Artificial Intelligence. From a cognitive point of view, it is also a key concept and researchers use it to explain the way we interact with our environment and the way we communicate. At present, the proliferationion of digital data and in particular, the great expansion of Internet reinforce the need for a concrete definition of this notion. Information is present, but users have to extract it from gigantic databases and the recurrent question is: which is the relevant information ?
In this document, we adopt a pluridisciplinarity approach to this notion which is oriented toward cognitive sciences. The noticeable diversity of problems and solutions suggested in different disciplines is explained and we put forward a solution which allows us to define " relevance " more globally. This solution is based on a construction and exploitation of an associative network. In this network, the nodes represent properties or conjunctions of properties, which are possibly fuzzy (in the fuzzy set theory sense) and connexions represent implications beetween these properties. We define a method for building a "fuzzy semantic network" from a set of observations. We also suggest a method for using this network in order to select information relative to a user's request.
The system which implements these methods (called RELIEFS), has been used for analysing different kinds of data (epidemics, sensorial evaluations). Lastly, a study shows how the system can be used in a Man-Machine communication context.
In this document, we adopt a pluridisciplinarity approach to this notion which is oriented toward cognitive sciences. The noticeable diversity of problems and solutions suggested in different disciplines is explained and we put forward a solution which allows us to define " relevance " more globally. This solution is based on a construction and exploitation of an associative network. In this network, the nodes represent properties or conjunctions of properties, which are possibly fuzzy (in the fuzzy set theory sense) and connexions represent implications beetween these properties. We define a method for building a "fuzzy semantic network" from a set of observations. We also suggest a method for using this network in order to select information relative to a user's request.
The system which implements these methods (called RELIEFS), has been used for analysing different kinds of data (epidemics, sensorial evaluations). Lastly, a study shows how the system can be used in a Man-Machine communication context.