We discuss the emerging applications of fuzzy logic and related technologies within the semantic web. Using fuzzy sets, we are able to provide an underlying semantics for linguistic concepts. We show how this framework allows for the representation of the types of imprecision characteristic of human conceptualization. We introduce some of the basic operations available for the representation and subsequent manipulation of knowledge. We illustrate the application of soft matching and searching technologies that exploit the underlying semantics provided by using fuzzy sets. We look at question-answering systems and point out how they differ from other information seeking applications, such as search engines, by requiring a deduction capability, an ability to answer questions by a synthesis of information residing in different parts of its knowledge base. This capability requires appropriate representation of various types of human knowledge, rules for locally manipulating this knowledge and framework for providing a global plan for appropriately mobilizing the information in the knowledge base to address the question posed. In this talk we suggest tools to provide these capabilities. We describe how the fuzzy set based theory of approximate reasoning can aid in the process of representing knowledge. We discuss how protoforms can be used to aid in deduction and local manipulation of knowledge. The concept of a knowledge tree is introduced to provide a global framework for mobilizing the knowledge in response to a query.
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