E1AR0002@SMUVM1.BITNET (09/29/86)
Design of Similarity-Based (Fuzzy) Relational Databases Speaker: Bill P. Buckles, University of Texas, Arlington Location 315 SIC, Southern Methodist University Time: 2:00PM While the core of an expert system is its inference mechanism, a common component is a database or other form of knowledge representation. The authors have developed a variation of the relational database model in which data that is linguistic or inherently uncertain may be represented. The keystone concept of this representation is the replacement of the relationship " is equivalent to" with the relationship "is similar to". Similarity is defined in fuzzy set theory as an $n sup 2$ relationship over a domain D, |D| = n such that i. s(x,x)=1, x member D ii. s(x,y)=s(y,x) x,y member D iii. s(x,y) >= max[min(s(x,y),s(y,z))]; x, y, z member D Beginning with a universal relation, a method is given for developing the domain sets, similarity relationships and base relations for a similarity-based relational database. The universal relation itself enumerates all domains. The domain sets may be numeric (in which case no further design is needed) or scalar (in which case the selection of a comprehensive scalar set is needed). Similarity relationship contains $n sup 2$ values where n is the number of scalars in a domain set. A method is described for developing a set of consistent values when initially given n-1 values. The base relations are derived using fuzzy functional dependencies. This step also requires the identification of candidate keys.