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 handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A framework for ontology evolution in collaborative environments
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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In this paper, we present the problem of ontology evolution and change management. We provide a systematic approach to solve the problem by adopting a multi-agent system (MAS). The core of our solution is the Semantic Relatedness Score (SRS) which is an aggregate score of five well-tested semantic as well as syntactic algorithms. The focus of this paper is to resolve current problems related to ontology upgrade and managing evolution amongst shared ontologies. This paper highlights issues pertaining to ontological changes in a shared ontology environment which includes creating, renaming, deletion and modification of existing classes. These changes will definitely impact shared concepts and users would have to update their local ontologies to be consistent with changes in the commonly shared ontology. We propose a less laborious method to achieve this by using a semi-automated approach where a bulk of the processing is carried out by matching agents that would eliminate extraneous data and hence would only recommend to the ontologist data that can actually be upgraded. We have also designed and built a prototype in the Java Agent DEvelopment Framework (JADE) for proof-of-concept.