A Knowledge Engineering Approach to Natural Language Understanding

A KNOWLEDGE ENGINEERING APPROACH TO NATURAL LANGUAGE UNDERSTANDING Stuart C. Shapiro & Jeannette G. Neal Department of Computer Science State University of New York at Buffalo Amherst, New York 14226 ABSTRACT This paper describes the results of a preliminary study of a Knowledge Engineering approach to Natural Language Understanding. A computer system is being developed to handle the acquisition, representation, and use of linguistic knowledge. The computer system is rule-based and utilizes a semantic network for knowledge storage and representation. In order to facilitate the interaction between user and system, input of linguistic knowledge and computer responses are in natural language. Knowledge of various types can be entered and utilized: syntactic and semantic; assertions and rules. The inference tracing facility is also being developed as a part of the rule-based system with output in natural language. A detailed example is presented to illustrate the current capabilities and features of the system. I INTRODUCTION This paper describes the results of a • preliminary study of a Knowledge Engineering (KE) approach to Natural Language Understanding (NLU). The KE approach to an Artificial Intelligence task involves a close association with an expert in the task domain. This requires making it easy for the expert to add new knowledge to the computer system, to understand what knowledge is in the system, and to understand how the system is accomplishing the task so that needed changes and corrections are easy to recognize and to make. It should be noted that our task domain is NLU. That is, the knowledge in the system is knowledge about NLU and the intended expert is an expert in NLU. The KE system we are using is the SNePS semantic network processing system [ii]. This system inci~ ~es a semantic network system in which ** This work was supported in part by the National Science Foundation under Grants MCS80-06314 and SPI-8019895. all knowledge, including rules, is represented as nodes in a semantic network, an inference system that performs reasoning according to the rules stored in the network, and a tracing package that allows the user to follow the system's reasoning. A major portion of this study involves the design and implementation of a SNePS-based system, called the NL-system, to enable the NLU expert to enter linguistic knowledge into the network in natural language, to have this knowledge available to query and reason about, and to use this knowledge for processing text including additional NLU knowledge. These features distinguish our system from other rule-based natural language processing systems such as that of Pereira and Warren [9] and Robinson [i0]. One of the major concerns of our study is the acquisition of knowledge, both factual assertions and rules of inference. Since both types of knowledge are stored in similar form in the semantic network, our NL-system is being developed with the ability to handle the input of both types of knowledge, with this new knowledge immediately available for use. Our concern with the acquisition of both types of knowledge differ~ from the …

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