In this volume, first we formulate a framework of fuzzy types to
represent both partial truth and uncertainty about concept and
relation types in conceptual graphs. Like fuzzy attribute values,
fuzzy types also form a lattice laying a common ground for
lattice-based computation of fuzzy granules. Second, for automated
reasoning with fuzzy conceptual graphs, we develop foundations of
order-sorted fuzzy set logic programming, extending the theory of
annotated logic programs of Kifer and Subrahmanian (1992). Third,
we show some recent applications of fuzzy conceptual graphs to
modelling and computing with generally quantified statements,
approximate knowledge retrieval, and natural language query
understanding.
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