A novel process-based KMS success framework empowered by ontology learning technology

Abstract

Organizations require empowered Information Systems (IS) and Knowledge Management Systems (KMS) to support several user’s knowledge-tasks and decision-making responsibilities. Gradually, some KMS applications founded on Artificial Intelligence and semantic technology (ontology-based) have emerged to accomplish more suitable KMS under appropriate quality levels. In parallel, the previous IS/KMS success frameworks commonly used to evaluate the demanded quality levels are just based on taxonomy of quality dimensions. In this work, we have suggested a novel Process-based KMS Success Framework which is enhanced by Ontology Learning technology. Thus, this new framework is based on a rationale combination of some quality and user dimensions of prior KMS Success Frameworks that are reinforced by a systemic hierarchy of Knowledge processes and their cyclical behaviour. Concretely, we propose a new KMS success framework that makes the best of the Ontology Learning (OL) technology to enhance the associated knowledge-process performance of this kind of KMS. A knowledge engineering case study has been included to illustrate the systemic enhancement and the results for the OL application under a specific domain using this novel framework. The OL methodological resources (MRs) such as methods, techniques, and tools used to introduce changes and to reach an empowered KMS implementation have been completed by applying an OL methodology.