Modelling Knowledge for a Natural Language Understanding System

Modelling Knowledge for a Natural Language Understanding System Gudrun glose, Thom,xs Pirlein IBM Germany Scientific Center Institute for Knowledge Based Systems P.O.Box 80 08 80 D-7000 Stuttgart 80 Emaih KLOSE@DSOLILOG, PIRLEIN@DSOLILOG Abstract In the field of knowledge based systems for natural lan- guage processing, one of the most challenging aims is to use parts of an existing knowledge base for differ- ent domains and/or different tasks. We support the point that this problem can only be solved by using ad- equate metainformation about the content and struc- turing principles of the representational systems con- cerned. One of the prerequisites in this respect is the transparency of modelling decisions. After a short introduction to our scenario, we will propose general dimensions for characterizing knowl- edge in knowledge based systems. These dimensions will be differentiated according to linguistic levels of investigation in order to deduce structuring principles for the modelling process. The resulting criteria will be evaluated in a detailed e~ample taken from our proto- typical implementation. We hope to contribute some promising steps towards a methodology of knowledge engineering with natural language and common sense orientation. 1 Introduction In the following, we wmlt to sketch first results of knowledge engineering research which was under- taken for the LILOG project (Linguistic and logic methods). LILOG develops concepts for natural language systems for text understanding. Major reslflts are available in a prototype system LEU/21 (I, ILOG Experimentier- Umgebung) 2. In order to reduce the complexity of the system, it has to be decomposed into modules. ! Leu/2 is being developped at IBM Germany in coopers- lion with some unlveralty partners, and il fully implemented in Prolog under AIX. The knowledge ba,e for the domain un- der investigation consists of about 600 concept definitions, among these some 100 belonging to the upper ltructure. The number of attributes for each of these concepts ever- ages around 20. At thlJ time the number of axioms for our domain it approximately 300. ~"LILOG Expetimelltal EnvlronmenC' Our approach embodies modules oriented to- wards levels oflingadstic investigation like morphol- ogy, syntax and semantics, h~ addition the modules differentiate between analysis and the generation processes. In the ideal case, all processes and mod- ules will be supported by conunonsense ]ulowledge. A crucial problem in tiffs context is the construc- tion of an adequ'ate background knowledge base. The: need for a methodology is obvious. First steps have been made in expert system research, where both domain andtask are for the most part clearly specifiable. This does not hold for systems with natural language - and conunon sense orientation. In what follows, we will outline the lulowledge en- gineering approach in LILOG along three dimen- sions. Task: Domain and te~cts were selected in order to cover a wide variety of lingalistic phenomena to be han- dled by the linguistic parts of the system (i.e. pars- lug:and generating components). Iat order to prove the;appropriate understanding of the texts, the at. chitecture was d~sigqled a.o. as a question/answer system. Hence, we get the additional …

Content truncated to 500 words