Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Representing and reasoning with set referents and numerical quantifiers
Conceptual structures
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
An overview of fuzzy quantifiers (II). Reasoning and applications
Fuzzy Sets and Systems
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
Universal Marker and Functional Relation: Semantics and Operations
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Conceptual Graphs: Draft Proposed American National Standard
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
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Conceptual graphs have been shown to be a logic that has a smooth mapping to and from natural language, in particular generally quantified statements, which is one of its advantages over predicate logic. However, classical semantics of conceptual graphs cannot deal with intrinsically vague generalized quantifiers like few, many, or most, which represent imprecise quantities that go beyond the capability of classical arithmetic. In this paper, we apply the fuzzy set-theoretic semantics of generalized quantifiers and formally define the semantics of generally quantified fuzzy conceptual graphs as probabilistic logic rules comprising only simple fuzzy conceptual graphs. Then we derive inference rules performed directly on fuzzy conceptual graphs with either relative or absolute quantifiers.