Ontology Mapping for Dynamic Multiagent Environment

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Author(s):
M. Jenifer, P.S. Balamurugan, T. Prince
Published Date:
April 30, 2012
Issue:
Volume 2, Issue 3
Page(s):
51 - 56
DOI:
10.7815/ijorcs.23.2012.029
Views:
4777
Downloads:
721

Keywords:
uncertain reasoning, multiagent systems, qos, ontology mapping, semantic, web service composition, web services
Citation:
M. Jenifer, P.S. Balamurugan, T. Prince, "Ontology Mapping for Dynamic Multiagent Environment". International Journal of Research in Computer Science, 2 (3): pp. 51-56, April 2012. doi:10.7815/ijorcs.23.2012.029 Other Formats

Abstract

Ontologies are essential for the realization of the Semantic Web, which in turn relies on the ability of systems to identify and exploit relationships that exist between and within ontologies. As ontologies can be used to represent different domains, there is a high need for efficient ontology matching techniques that can allow information to be easily shared between different heterogeneous systems. There are various systems were proposed recently for ontology mapping. Ontology mapping is a prerequisite for achieving heterogeneous data integration on the Semantic Web. The vision of the Semantic Web implies that a large number of ontologies present on the web need to be aligned before one can make use of them. At the same time, these ontologies can be used as domain-specific background knowledge by the ontology mapping systems to increase the mapping precision. However, these ontologies can differ in representation, quality, and size that pose different challenges to ontology mapping. In this paper, we analyzed the various challenges of recently introduced Multi-Agent Ontology Mapping Framework, DSSim and we have integrated an efficient feature called QoS-Web Services Composition with DSSsim. ie we have improved this framework with QoS based Service Compositions Mechanism. From our experimental results, it is established that this developed QoS based Web Services Compositions Mechanism for Multiagent Ontology Mapping Framework minimizing uncertain reasoning and improves matching time, which are encouraging results of our proposed work.

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