Abstract
Knowledge Representation (KR) is an emerging field of research in AI and Data Mining. Knowledge represented in an effective way guarantees a good retrieval. In this regard, a number of effective approaches have been proposed in the literature and Semantic Networks (SN) are one of them. In SN knowledge is represented in the form of directed graph, where concepts and relationships are appeared at vertices and edges respectively. 'is a' is one of the most frequently used relationship in SN. 'is a' expresses the exact relationship between any pair of objects. But there exists a huge amount of knowledge that cannot be represented by just 'is a', like the knowledge where approximations are involved. To overcome this issue, fuzzy semantic networks (FSN) are proposed in this chapter. In FSN 'is a' is replaced by a fuzzy membership function 'mu' having value between [0,1]. So the relationship between a pair of concepts can be expressed as a certain degree of membership. This chapter focuses on applications of FSN and its significance over the traditional SN.