UM MODELO DE RECUPERAÇÃO DE INFORMAÇÃO PARA A WEB SEMÂNTICA.

Abstract

Several techniques for extracting meaning from text in order to construct more accurate internal representations of both queries and information items in retrieval systems have been already proposed. However, there is a lack of semantic retrieval models to provide appropriate abstractions of these techniques. This work proposes a knowledge--based information retrieval model that explores the semantic content of information items . The internal representation of information items is based on user interest groups, called “semantic cases”. The model also defines a criteria for retrieve information items and a function for ordering the results that uses similarity measures based on semantic distance between semantic cases items. The model was instantiated by a sample system built upon the tributary legal domain using the specialization of the ONTOJURIS, a generic legal ontology, called ONTOTRIB. Legal normative instruments can be instantiated in a knowledge base by ONTOTRIB classes. The results obtained for this specific domain showed an improvement in the precision rates compared to a keyword-based system.