Cognitively Relevant Schemas for Knowledge Representation

Abstract

this paper is to look at the psychological basis of representations used for world and lexical knowledge in NLP systems. The tacit assumption underlying this endeavour is that using a representation formalism which is psychologically valid is a Good Thing. From the perspective of many computational linguists, however, such psychological validity may not seem useful because of the vagueness associated with psychological models. Why worry about what form the representations take, they will argue, when there are systems capable of handling a wide array of tasks: anaphora resolution, quantifier scoping, query resolution, inferencing,: : : ? The reason is that there is only one system which can handle all the tasks associated with natural language -- the human mind. Current computational systems are hampered by a lack of uniformity. Issues that go beyond first order logic (modals, nonmonotonistic reasoning, etc.) are dealt with in an ad hoc way. There is no possibility of integrating the assumptions and the representations of the various systems to create a system capable of general natural language understanding. What is clearly needed is a uniform representational formalism with an explicitly defined semantics which can provide the basis for the development of systems capable of handling the range of phenomena in natural language understanding. John Sowa has argued that his conceptual graph formalism should be adopted as the worldwide standard for representation of semantic knowledge. A large part of this paper will be devoted to a description and evaluation of this formalism