Knowledge Management Processes Supported by Ontology Technologies

Knowledge Management Processes Supported by Ontology Technologies

Alexandra Pomares-Quimbaya (Pontificia Universidad Javeriana, Colombia) and Miguel Eduardo Torres-Moreno (Pontificia Universidad Javeriana, Colombia)
DOI: 10.4018/978-1-4666-1993-7.ch007
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Abstract

Although ontologies have been used in a considerable number of knowledge management projects, in most of them their application has been limited to the definition of a common organizational language. This chapter offers an alternative view on the use of ontologies, supporting all of the knowledge management processes, including generation, acquisition, transference, and integration of knowledge. The focus of this chapter is on presenting how ontologies can be included in knowledge management systems to promote availability of knowledge, knowledge evolution, automatic and semi-automatic knowledge extraction, and integration of heterogeneous sources of information. Each one of these characteristics is supported by ontology technologies including the processes required to engineer and manage an ontology, such as: querying, mapping, and evolution. Finally, the architecture of a proposed knowledge management system that uses ontologies to improve its processes is presented.
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An ontology “...defines the basic terms and relationships comprising the vocabulary of a topic area, as well as the rules for combining terms and relationships to define extensions to the vocabulary...” (Corcho, Fernández-López, & Gómez-Pérez, 2003). In general, ontologies are conceptualizations of a particular domain of interest (Gruber, 1995), they are used as common models that are shared and integrated among many systems or applications. The application of ontologies in knowledge management enterprise solutions implies an adequate handling of the fundamental processes of the ontology life cycle. This section presents what kind of ontologies can be used to improve knowledge management systems and then, how these ontologies must be managed in order to assure their usefulness in knowledge management systems.

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