The requirement of semantic technology has been augmented day by day in IT related applications due to various features including interoperability. Effective development and organization of semantic knowledge are essential for maximum throughput in any application. We extend and develop two sub-domain computer resource ontologies and utilize them in a resource discovery process of a Grid computing environment to reduce the job rejection rate. Grid computing aggregates distributed computing resources to execute computationally complex jobs. The selection of resources in a Grid system involves finding and locating resources based on users' requirements. Identifying an appropriate resource selection mechanism for Grid jobs is a major concern because overall performance of a Grid depends on it and it also helps to schedule and allocate resources. We compute semantic similarity threshold values and employ both extended ontolgies in a decentralized resource discovery model of Grid Computing. The simulation is carried out using GridSim and PlanetSim to evaluate the effectiveness of a semantic resource discovery model. The results show improved success probability for complex jobs and reduce communication overheads compared to the non semantic resource discovery model.
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