Computer Science Faculty Publications


Parsimonious Covering as a Method for Natural Language Interfaces to Expert Systems

Document Type



Abductive inference has been characterized in the AI literature as ‘inference to the best explanation’ or as ‘plausible inference involving context-sensitive discrimination among explanatory hypotheses’. Analogously, understanding natural language involves context-sensitive discrimination among word senses, and there has been a growing awareness that it can be viewed as a type of abductive inference. Parsimonious covering theory, first formulated to model the abductive inference underlying medical diagnostic problem solving, is examined here as a method for automating natural language processing for medical expert system interfaces. The nature of ‘parsimony’ in natural language processing and the relationship of parsimonious covering to a notion of focus of attention are discussed.

An experimental prototype developed to test these ideas in the context of a medical expert system is briefly described. This prototype is domain-independent in the same sense that a generic expert system shell is domain-independent. Given a knowledge base for a specific medical application, a vocabulary extractor extracts and indexes the linguistic information which it contains. In addition, an indexed domain-independent knowledge base that contains linguistic knowledge common to many domains is used. With a parsimonious covering inference mechanism superimposed on this knowledge, a natural language interface is generated for the specific application defined by the knowledge base.

Publication Date


Publication Title

Artificial Intelligence In Medicine


Start Page No.


End Page No.


This document is currently not available here.