A novel integrated knowledge support system based on ontology learning: Model specification and a case study

  • Authors:

  • R. J. Gil;M. J. Martin-Bautista

  • Affiliations:

  • Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

Semantic engineering is currently being adopted to support the knowledge-management processes needed by organizational users for decision-making and task-intensive knowledge activities. Such optional engineering strategies consider that some systems, such as the Knowledge Support System (KSS) fulfill the needs of the knowledge user, by providing the services and management qualities they require. Some key features of the KSS have been analyzed to identify their main characteristics or system components according to the most recent trends. Lately, some solutions have been proposed to develop this type of knowledge system based on the approaches, Ontology Development and Ontology Learning (OL). In this paper, a novel model of an Ontology-Learning Knowledge Support System (OLeKSS) is proposed to keep these KSSs updated. The proposal applies concepts and methodologies of system modeling as well as a wide selection of OL processes from heterogeneous knowledge sources (ontologies, texts, and databases), in order to improve KSS's semantic product through a process of periodic knowledge updating. An application of a Systemic Methodology for OL (SMOL) in an academic Case Study illustrates the enhancement of the associated ontologies through process of population and enrichment.