Using Semantic Negotiation for Ontology Enrichment in e-Learning Multi-agent Systems

Abstract

In this work, we propose an algorithm, called E-Learning Ontology Enrichment (ELOE), to derive a global representation from the personal ontologies of different agents present in an e-Learning MAS. Using ELOE, each agent of a MAS-based e-Learning system can autonomously enrich its own ontology by using semantic negotiation and, at the same time, it can access to the global ontology to have a view of the terms used by all the other agents. Each term of the global ontology is associated with a set of meanings, and each meaning is associated with the set of agents that have used it. This way an agent, having to send a message to an interlocutor, is able of choosing from the global ontology the most suitable term with the most appropriate meaning. Only if the agent does not find in the global ontology the appropriate term, it will use a personal term that probably will lead to a new semantic negotiation process. This way, the use of the onerous task of the semantic negotiation will be limited to only the strictly necessary situations, and consequently the whole communication cost is decreased.