A Semantic Knowledge Management System for Government Repositories

Nowadays, in a knowledge-driven economy, the organizations, public or private, require to make an appropriate managing knowledge assets to sustain competitive edge in global markets or in governmental services. Advances in Information and Communications Technologies have supported innovations in Knowledge Management (KM). Since the KM has been recognized as one of the critical factors for obtaining organizational competitiveness. KM is among the promising areas for the application of Semantic Web. In this paper we propose an approach to the development of a technological platform for KM that integrates semantic Web technologies. This platform supports all processes required in knowledge management. Therefore, the platform enables the automatic identification of patterns and generation of the taxonomies from unstructured texts (documents). The platform consists of tools that allow storing documents (from a knowledge contributor interface), processing of source documents, i.e. identifying sentences, stop-words, identifying n-grams, and generating bags of words, denoting lexical patterns, taxonomic-relation extraction, and inference algorithms to retrieve knowledge from a set of repositories. These algorithms use the previous phases to retrieve knowledge more accurately derived from the semi-structured or structured information previously generated.