Classification of the state-of-the-art dynamic web services composition techniques
International Journal of Web and Grid Services
A data-oriented survey of context models
ACM SIGMOD Record
Mobile service oriented architectures for NN-queries
Journal of Network and Computer Applications
Ontology tailoring in the Semantic Grid
Computer Standards & Interfaces
Towards a Framework for Workflow Composition in Ontology Tailoring in Semantic Grid
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Ontology driven semantic profiling and retrieval in medical information systems
Web Semantics: Science, Services and Agents on the World Wide Web
Techniques for discovering correspondences between ontologies
International Journal of Web and Grid Services
A standard ontology for smart spaces
International Journal of Web and Grid Services
International Journal of Web and Grid Services
Vietnamese Knowledge Base development and exploitation
International Journal of Business Intelligence and Data Mining
A grid application service framework for extracted sub-ontology update
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Ontology construction using online ontologies based on selection, mapping and merging
International Journal of Web and Grid Services
Information Systems Frontiers
Ontology as a Service (OaaS): a case for sub-ontology merging on the cloud
The Journal of Supercomputing
Ontological modelling of form and function for architectural design
Applied Ontology
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The use of ontologies lies at the very heart of the newly emerging era of semantic web. Ontologies provide a shared conceptualization of some domain that may be communicated between people and application systems. As information on the web increases significantly in size, web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. Moreover, because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology becomes very computationally extensive. Therefore, it is imperative that parallel and distributed computing techniques be utilized to implement the extraction process. These problems have stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. The extraction process consists of a number of independent optimization schemes that cover various aspects of the optimization process, such as ensuring consistency of the user-specified requirements for the sub-ontology, ensuring semantic completeness of the sub-ontology, etc. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Our proposed and implemented framework for the extraction process, referred to as Materialized Ontology View 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. We utilize coarse-grained data-level parallelism inherent in the problem domain. Such an 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. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of ontology-based information retrieval.