Authors:
Miklos Nagy
1
and
Maria Vargas-Vera
2
Affiliations:
1
Knowledge Media Institute,The Open University, United Kingdom
;
2
The Open University, United Kingdom
Keyword(s):
Multi-agents, Fuzzy systems, Voting, Conflict resolution.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Collaboration and e-Services
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
e-Business
;
Enterprise Information Systems
;
Fuzzy Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Semantic Web
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
Software agents that interpret the possible meaning of SemanticWeb data differently should be able to resolve their differences i.e. resolve conflicts effectively. One typical use case is ontology mapping where different agents using different similarity measures create beliefs in the assessed similarities, which needs to be combined into a more coherent state. The combination of these contradicting beliefs can easily worsen the mapping
precision and recall, which leads to poor performance of any ontology mapping algorithm. In these scenarios agents, which use different similarities and combine them into a more reliable and coherent view can easily
become unreliable when these contradictions are not managed effectively between the different agents. In this paper we propose a solution based on the fuzzy voting model for managing such situations by introducing
trust and voting between software agents that resolve contradicting beliefs in the assessed similarities.