Discovery of Class Relations in Exception Structured Knowledge Bases

Abstract

Abstract. Knowledge-based systems (KBS) are not necessarily based on a well-defined ontologies. In particular it is possible to build very successful KBS for classification problems, but where the classes or conclusions are entered by experts as free-text sentences with little constraint on textual consistency and little systematic organisation of the conclusions. This paper investigates how relations between such 'classes' may be discovered from existing knowledge bases. We have based our approach on KBS built with Ripple Down Rules (RDR). RDR is a knowledge acquisition and knowledge maintenance method which allows KBS to be built very rapidly and simply by correcting errors, but does not require a strong ontology. Our experimental results are based on a large real-world medical RDR KBS. The motivation for our work is to allow an ontology in a KBS to 'emerge' during development, rather than requiring the ontology to be established prior to the development of the KBS. It follows earlier work on using Formal Concept Analysis (FCA) to discover ontologies in RDR KBS.

Two decades of Ripple Down Rules research

“…The technique offered an alternative to labour-intensive specification of ontologies that combined rapid, incremental and manual KA using RDR with automatic generation of ontologies using FCA to enable reverse engineering of ontologies (Richards, 2004). Suryanto and Compton (2000) took this work further to allow learning of classification taxonomies and the relations of mutual-exclusivity and similarity between classes in addition to the subsumption relation provided by FCA.…”