Reasoning about Knowledge Using Rough Sets
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Combining Uncertain Outputs from Multiple Ontology Matchers
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Integrating uncertainty into ontology mapping
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
| Hi-index | 0.01 |
The book systematically provides the reader with a broad range of systems/research work to date that addresses the importance of combining numerical and symbolic approaches to reasoning under uncertainty in complex applications. It covers techniques on how to extend propositional logic to a probabilistic one and compares such derived probabilistic logic with closely related mechanisms, namely evidence theory, assumption-based truth maintenance systems and rough sets, in terms of representing and reasoning with knowledge and evidence. The book primarily addresses researchers, practitioners, students and lecturers in the field of Artificial Intelligence, particularly in the areas of reasoning under uncertainty, logic, knowledge representation and reasoning, and non-monotonic reasoning.