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An Ontology-Learning Knowledge Support System to Keep e-Organization’s Knowledge Up-to-Date: A University Case Study

  • Richard J. Gil
  • Maria J. Martín-Bautista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6866)

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.

Keywords

Knowledge Management Recommender System Relational Database Knowledge Source Knowledge Management System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Richard J. Gil
    • 1
  • Maria J. Martín-Bautista
    • 1
  1. 1.Dept. of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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