Expert systems are strongly characterised by their use of a large collection of domain specific knowledge acquired from human experts. Several data structures, have been proposed and used in the past for storing this knowledge. Some of the popularly used structures are (i) Frames, (ii) Scripts, (iii) Semantic Nets and (iv) Production rules. Knowledge acquired from the expert over a length of time tends to be inconsistent and redundant thus requiring enormous amount of storage space. Clustering algorithms can be successfully employed to modify the knowledge base so as to make it concise and consistent. Similarity measure has been defined between patterns represented as (i) production rules and semantic Nets and used for clustering. Frame is a structure that captures the hierarchical relationship between several concepts characterizing a pattern and/or subpatterns. Frame is viewed as a collection of concepts which are related to one another through a binary predicate. Clustering is used to group various patterns belonging to a set of classes and obtain a super concept. Similarity between two patterns is defined using a linear structure corresponding to the original hierarchical representation. The above conversion is obtained using a suitable tree-traversal scheme. Several operations defined on the hierarchical data structure is used to define the similarity measure among pattern/pattern classes. The similarity measure between patterns represented as concept frames is used along with a hierarchical agglomerative clustering algorithm to reduce the size of the knowledge base.

Date Published: 26 March 1986
PDF: 7 pages
Proc. SPIE 0635, Applications of Artificial Intelligence III, (26 March 1986); doi: 10.1117/12.964156

Published in SPIE Proceedings Vol. 0635:
Applications of Artificial Intelligence III
John F. Gilmore, Editor(s)