An Ontology-Learning knowledge support system to keep e-organization's knowledge up-to-date: A university case study

Abstract

e-Organizational users can apply semantic engineering solutions to deal with decision-making and task-intensive knowledge requirements supported by Knowledge Management Systems (KMSs). Such optional engineering strategies consider some system types to meet knowledge users' need, aligned with the e-services and e-management qualities required for them. Particularly, in the Knowledge Support System (KSS) field, developers have adopted some Ontology-based technologies to support user's task-knowledge system functionalities. In this paper, an Ontology-Learning Knowledge Support System (OLeKSS) model is proposed as a general component of e-organizations, to keep the ontologies associated with this kind of KMS updated and enriched. Relational Databases (RDBs) are considered complementary knowledge source for Knowledge Acquisition (KA) through a OLeKSS Process (as a subsystem component) based on methodologies for Ontology Learning (OL). In a University case, we had applied a Systemic Methodology for OL (SMOL) from a RDB to update the correspondent host-ontology associated to the University's KSS during this OLeKSS process. © 2011 Springer-Verlag Berlin Heidelberg.