Prolog: the standard: reference manual
Prolog: the standard: reference manual
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Logic, Programming, and PROLOG
Logic, Programming, and PROLOG
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Query answering in rough knowledge bases
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
RoSy: a rough knowledge base system
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Transactions on Rough Sets IV
A framework for reasoning with rough sets
Transactions on Rough Sets IV
Paraconsistent Logic Programs with Four-Valued Rough Sets
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
Four-valued extension of rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
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This paper extends the basic rough set formalism introduced by Pawlak [1] to a rule-based knowledge representation language, called Rough Datalog, where rough sets are represented by predicates and described by finite sets of rules. The rules allow us to express background knowledge involving rough concepts and to reason in such a knowledge base. The semantics of the new language is based on a four-valued logic, where in addition to the usual values TRUE and FALSE, we also have the values BOUNDARY, representing uncertainty, and UNKNOWN corresponding to the lack of information. The semantics of our language is based on a truth ordering different from the one used in the well-known Belnap logic [2, 3] and we show why Belnap logic does not properly reflect natural intuitions related to our approach. The declarative semantics and operational semantics of the language are described. Finally, the paper outlines a query language for reasoning about rough concepts.