Case-Based Reasoning within Tabular Systems. Extended Structural Data Representation and Partial Matching

Abstract

One of the key issues for efficient application of Case-Based Reasoning (CBR) methodology is to define an appropriate, powerful knowledge representation structure for case encoding and definition of flexible matching algorithms. The basic paradigm consisting in straightforward application of Relational Databases, although conceptually simple and computationally efficient, suffers from inflexible matching algorithms and the lack of possibilities to represent more complex data structures. This paper provides an analysis of certain possible extensions of data structures to be represented within the widely accepted relational database like paradigm, here called Tabular Systems, while providing new possibilities to represent quite complex patterns. As the basic underlying structure the concept of an object is used. A review of selected non-atomic data items, i.e. structures such as sets, record-like structures, lists, intervals, terms, trees, graphs etc. is provided. Flexibility of this structures comes from the fact that they can cover a number of particular cases. For such structures flexible partial matching procedures are proposed. The flexibility is achieved through admission of several types (levels) of matching, different with respect to qualitative perception of the level of similarity. Such flexible matching of data structures provides a way for generation of a match both with respect to parameter adjustment and structure manipulation.

Research supported from a KBN Grant No.: 8 T11C 019 17 „Formal Methods and Tools for Support of Analysis and Design of Databases and Knowledge Bases“.