Semantic Web Enabled Resource Scheduler: An Approach using Temporal Extensions to Ontologies

Abstract

The semantic Web and agent technologies have provided new possibilities and challenges to automatic information processing. This paper presents the architecture of semantic Web enabled resource scheduler that uses agent technology and semantic web framework to perform assigned tasks of scheduling with minimal human intervention. Temporal extensions to ontologies are used for capturing change in knowledge and thereby providing information to the scheduler agent for taking the appropriate decision. For retrieving the temporal knowledge new clauses are proposed to enhance SPARQL. A formal approach is presented for using temporal knowledge to empower the system with self learning capability. Academic event scheduler is implemented to validate the architecture of resource scheduler and analyzed results are presented. The presented architecture is scalable, portable, and modular and can be customized for any domain.