Application of Ontology for Effective Management of Knowledge in an E-learning Environment

By:
Bhavani Sridharan,
Prof. Bill Martin,
Assoc Prof. Hepu Deng
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In the current Internet era, a systematic approach to managing knowledge is an essential pre-requisite to enable learners and knowledge seekers to access relevant material as and when it is required. To enable effective e-learning, ontologies can play a crucial role by identification of critical ontology factors in management of knowledge with respect to various learning strategies. While ontologies and the semantic web, which provides for semantic links, are increasingly used in business and research, their acceptance in e-learning is still at the preliminary stages. This paper reviews the relevant literature for identification of critical ontology factors to enable efficient reuse and sharing of knowledge in an e-learning environment.


Keywords: Knowledge Management, E-learning, Ontology
Stream: Pedagogies and Teaching Practices
Presentation Type: 30 minute Paper Presentation in English
Paper: , William Kentridge and the Black Box of German Colonialism, Ontology-based E-learning Model for Effective Management of Learning Resources, An

Bhavani Sridharan

Research Student, Business Information Technology, RMIT University
Melbourne, Victoria, AUSTRALIA

My areas of interest include knowledge management, ontologies and sematic web, e-learning and database management.

Prof. Bill Martin

Professor and Director of Research, School of Business Information Technology, RMIT university
AUSTRALIA

Professor Bill Martin is Director of Research at the School of Business IT, RMIT University in Melbourne. A leading knowledge management player he is active nationally and internationally as an author, speaker and facilitator.

Assoc Prof. Hepu Deng

Associate Professor, School of Business Information Technology, RMIT University
AUSTRALIA

Dr Hepu Deng is an associate professor at the School of Business Information Technology, RMIT University, Australia. His research interests include multicriteria analysis, neural networks, approximate reasoning, fuzzy logic,
preference modelling, knowledge management and their applications in business.

Ref: LS7P0029