This work introduces a conceptual framework and its current implementation to support the classification and discovery of knowledge sources, where every knowledge source is represented through a vector (named Semantic Vector - SV). The novelty of this work addresses the enrichment of such knowledge representations, using the classical vector space model concept extended with ontological support, which means to use ontological concepts and their relations to enrich each SV. Our approach takes into account three different but complementary processes using the following inputs: (1) the statistical relevance of keywords, (2) the ontological concepts, and (3) the ontological relations. SVs are compared against each other, in order to obtain their similarity index, and better support end users with a search/retrieval of knowledge sources capabilities. This paper presents the technical architecture (and respective implementation) supporting the conceptual framework, emphasizing the SV creation process. Moreover, it provides some examples detailing the indexation process of knowledge sources, results achieved so far and future goals pursued here are also presented.
|Title of host publication||Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD 2012)|
|Publication status||Published - 1 Jan 2012|
|Event||KEOD 2012 - International Conference on Knowledge Engineering and Ontology Development - |
Duration: 1 Jan 2012 → …
|Conference||KEOD 2012 - International Conference on Knowledge Engineering and Ontology Development|
|Period||1/01/12 → …|