A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

Author:

CAO TRU H.1

Affiliation:

1. Department of Information Technology, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet St., Dist. 10 HCMC, Vietnam

Abstract

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Information Systems,Control and Systems Engineering,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Conceptual Graphs and Fuzzy Logic;Studies in Computational Intelligence;2010

2. Language Structure Using Fuzzy Similarity;IEEE Transactions on Fuzzy Systems;2009-10

3. Ontological approach to development of computing with words based systems;International Journal of Approximate Reasoning;2009-01

4. SUBSUMPTION DEGREES BETWEEN ENTITY TYPES AND NAMES FOR APPROXIMATE KNOWLEDGE RETRIEVAL;International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems;2007-02

5. Chapter 14 Approximate knowledge graph retrieval: Measures and realization;Fuzzy Logic and the Semantic Web;2006

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