Authors:
Paulo Figueiras
1
;
Ruben Costa
2
;
Luis Paiva
3
;
Celson Lima
4
and
Ricardo Jardim-Gonçalves
2
Affiliations:
1
UNINOVA, Portugal
;
2
UNINOVA and Universidade Nova de Lisboa, Portugal
;
3
Universidade Nova de Lisboa, Portugal
;
4
Federal University of Western Pará, Brazil
Keyword(s):
Information Retrieval, Ontology Engineering, Knowledge Representation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Process Knowledge and Semantic Services
;
Symbolic Systems
Abstract:
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 creat
ion 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.
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