Agent-based semantic interoperability in infosleuth
ACM SIGMOD Record
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Making Peer Databases Interact - A Vision for an Architecture Supporting Data Coordination
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Ontologies for semantically interoperable systems
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Information systems interoperability: What lies beneath?
ACM Transactions on Information Systems (TOIS)
Virtual organization security policies: An ontology-based integration approach
Information Systems Frontiers
A framework for ontology evolution in collaborative environments
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
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In this paper, we present a hybrid similarity matching algorithm i.e. Semantic Relatedness Score (SRS) that is used to match ontological concepts and instances in the context of ontology evolution. We combine five out of thirteen well-tested semantic and syntactic algorithms to produce SRS. Specifically we focus on the issue of ontology upgrade and highlight how our hybrid matching algorithm produces higher precision and reliability compared to existing syntactical approaches. Managing evolution amongst shared ontologies is a laborious affair. This paper extends ongoing work in the area and focuses on changes made in a shared ontology such as inclusion of new classes, renaming, deletion and modification of existing classes. Since these changes will adversely impact shared concepts, users would have to update their local ontologies to be consistent with changes in the shared ontology. As such, a less laborious method is needed for an ontologist to update his local ontology. This paper illustrates how a multi-agent system (MAS) can be deployed to achieve this goal. A semi-automated approach is presented where agent systems would filter concept changes based on SRS and recommend them to the ontologist for upgrade. A MAS prototype has been implemented in the Java Agent DEvelopment Framework (JADE) which is a FIPA standard for proof-of-concept.