The construction of consistent possibility and necessity measures
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
The description logic handbook: theory, implementation, and applications
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Testing the descriptive validity of possibility theory in human judgments of uncertainty
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
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Extending Description Logics with Uncertainty Reasoning in Possibilistic Logic
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Managing uncertainty and vagueness in description logics for the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
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Uncertainty Reasoning for the Semantic Web I
Inconsistencies, negations and changes in ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Inferring a possibility distribution from empirical data
Fuzzy Sets and Systems
Possibility and necessity functions over non-classical logic
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A notion of comparative probabilistic entropy based on the possibilistic specificity ordering
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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This paper focuses on the application of possibilistic logic in the extension of ontology for uncertain knowledge description. It proposes an uncertain semantic relationship description method based on possibilistic logic and probabilistic statistics. This novel method combines the advantages of subjective valuation and objective statistics, which makes the representation and inference of uncertainty in ontologies more flexible and reasonable. Finally, we apply this quantitative measurement to describe incomplete uncertain knowledge and imprecise vague information of uncertain knowledge in ontology knowledgebase.