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- Bhatt, Mehul, Professor, 1980-, et al.
(author)
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A distributed approach to sub-ontology extraction
- 2004
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In: 18th International Conference on Advanced Information Networking and Applications (AINA 2004), 29-31 March 2004, Fukuoka, Japan. - Los Alamitos : IEEE Computer Society. - 0769520510 - 9780769520513 ; , s. 636-641
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Conference paper (peer-reviewed)abstract
- The new era of semantic web has enabled users to extract semantically relevant data from the web. The backbone of the semantic web is a shared uniform structure which defines how web information is split up regardless of the implementation language or the syntax used to represent the data. This structure is known as an ontology.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. This has stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology.Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. 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 can be very extensive. Therefore we have identified the need for a distributed approach to the extraction process. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of information retrieval.
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| 2. |
- Bhatt, Mehul, Professor, 1980-, et al.
(author)
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MOVE : A Distributed Framework for Materialized Ontology View Extraction
- 2006
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In: Algorithmica. - : Springer. - 0178-4617 .- 1432-0541. ; 45:3, s. 457-481
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Journal article (peer-reviewed)abstract
- 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.
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| 3. |
- Bhatt, Mehul, Professor, 1980-, et al.
(author)
-
Semantic Completeness in Sub-ontology Extraction Using Distributed Methods
- 2004
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In: Computational Science and Its Applications - ICCSA 2004. - Berlin, Heidelberg : Springer. - 9783540220572 - 9783540247678 ; , s. 508-517
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Conference paper (peer-reviewed)abstract
- 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, 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. 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.
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