Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Introduction to Multiagent Systems
Introduction to Multiagent Systems
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
Ontology mapping: the state of the art
The Knowledge Engineering Review
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
Calculating optimal decision using meta-level agents for multi-agents in networks
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Reaching agreement over ontology alignments
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Web and semantic web query languages: a survey
Proceedings of the First international conference on Reasoning Web
Framework for dynamic life critical situations using agents
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Development of an enterprise decision platform: Service-Oriented Architecture approach
International Journal of Intelligent Information and Database Systems
Comparing ontologies using multi-agent system and knowledge base
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IV
Expert Systems with Applications: An International Journal
A multi-agent system with negotiation agents for e-trading products and services
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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The semantic web uses ontologies to improve searching since ontologies provide a richer semantic model of content by expressing terms and relationships. However, a problem with the web is the large number of ontologies, which complicates searching. To maximize the capability of the search, the ontologies need to be combined to obtain complex answers. Usually, the ontologies are created without following any guidelines and, therefore, combining them is not a simple task, especially when ensuring a consistent result. We propose using meta-agents on top of software agents in a multi-agent system to resolve the use and the combination of multiple ontologies and to enable searching and reasoning. The software agents search for parts in the ontologies corresponding to the user-request and meta-agents combine the results from the agents. The meta-agents also partition the ontologies into consistent sets and then combine multiple ontologies into meaningful and logically consistent structures. For this purpose, we extend an existing mapping and alignment algorithm used for communication between agents. The use of multi-agents gives advantages since they provide a parallel approach and, thereby, efficiently handle large numbers of ontologies in order to accomplish tasks separately.