An Analogy reasoning model for Semantic Link Network

Analogy reasoning is one of the most important reasoning means of human thinking. How to implement analogy reasoning automatically with computers has been a hot topic in artificial intelligence and psychology. This paper proposes a mathematic model for analogy reasoning over semantic link network (SLN). We propose a reliable analogy theorem over SLN based on the category theory and some algorithms have been developed to implement analogical reasoning by constructing a semantic functor between SLNs. Meanwhile, we also discuss some analogy conjecture models and algorithms which may be unreliable but useful to giving some suggestions for solving a given problem. A study cases is proposed to show the validity and efficiency of the proposed theorem and the algorithms