Abstract
Reasoning by analogy refers to recognizing certain similarities between a source situation/object and a target situation/object and deriving some properties of the target on the basis of observed similarities with the source. Analogy is an important inference tool in human cognition and is a powerful computational tool for general inference. Null queries are queries that elicit a null answer from the database system, often because of the incompleteness of information in the database, for example, the absence of certain attribute values for some objects. Analogy is useful for obtaining an approximate answer to a null query. In this paper, we develop the theoretical basis for the application of analogical reasoning to obtain approximate answers for null queries in the context of a fuzzy relational data model. The incorporation of analogical reasoning in data models enhances their user-friendliness. Our proposed model of analogy incorporates fuzzy logic and is a natural generalization of models of analogy researched in the domain of artificial intelligence.
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