Semantic Completeness in Sub-ontology Extraction Using Distributed Methods

The use of ontologies lies at the very heart of the newly emerging era of Semantic Web. They provide a shared conceptualization of some domain that may be communicated between people and application systems. A common problem with web ontologies is that they tend to grow large in scale and complexity as a result of ever increasing information requirements. The resulting ontologies are too large to be used in their entirety by one application. Our previous work, M aterialized Ontology V iew Extractor (MOVE), has addressed this problem by proposing a distributed architecture for the extraction/optimization of a sub-ontology from a large scale base ontology. The extraction process consists of a number of independent optimization schemes that cover various aspects of the optimization process. In this paper, we extend MOVE with a Semantic Completeness Optimization Scheme (SCOS), which addresses the issue of the semantic correctness of the resulting sub-ontology. Moreover, we utilize distributed methods to implement SCOS in a cluster environment. Here, a distributed memory architecture serves two purposes: (a). Facilitates the utilization of a cluster environment typical in business organizations, which is in line with our envisaged application of the proposed system and (b). Enhances the performance of the computationally extensive extraction process when dealing with massively sized realistic ontologies.