Mass Assignment Fundamentals for Computing with Words
IJCAI '97 Selected and Invited Papers from the Workshop on Fuzzy Logic in Artificial Intelligence
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
MICAI '06 Proceedings of the Fifth Mexican International Conference on Artificial Intelligence
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Measuring similarity between semantic business process models
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
Managing Conflicting Beliefs with Fuzzy Trust on the Semantic Web
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size
Web Semantics: Science, Services and Agents on the World Wide Web
A survey of trust in internet applications
IEEE Communications Surveys & Tutorials
Ontology Matching: State of the Art and Future Challenges
IEEE Transactions on Knowledge and Data Engineering
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This paper introduces a novel trust assessment formalism for contradicting evidence in the context of multi-agent ontology mapping. Evidence combination using the Dempster rule tend to ignore contradictory evidence and the contemporary approaches for managing these conflicts introduce additional computation complexity i.e. increased response time of the system. On the Semantic Web, ontology mapping systems that need to interact with end users in real time cannot afford prolonged computation. In this work, we have made a step towards the formalisation of eliminating contradicting evidence, to utilise the original Dempster's combination rule without introducing additional complexity. Our proposed solution incorporates the fuzzy voting model to the Dempster-Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem.