DCKR – Knowledge Representation in Prolog and Its Application to Natural Language Processing

DCKR -- Knowledge Representation in Prol0g and Its Application to Natural Language Processing Hozumi Tanaka Tokyo Institute of Technology Dept. of Computer Science O-okayama, 2-12-1, Megro-ku Tokyo, Japan ABSTRACT: Semantic processing is one of the important tasks for natural language processing. Basic to semantic processing is descriptions of lexical items. The most frequently used form of description of lexical items is probably Frames or Objects. Therefore in what form Frames or Objects are expressed is a key issue for natural language processing. A method of the Object representation in Prolog called DCKR will be introduced. It will be seen that if part of general knowledge and a dictionary are described in DCKR, part of context- processing and the greater part of semantic processing can be left to the functions built in Prolog. 1. Introduction Relationships between knowledge represented in predicate logic formulas and knowledge represented in Frames or ~Kt~i~K~ fihi~i~ are clarified by [Hayes 80], [Nilsson 80], [Goebel 85],[Bowen 85], et al, but their methods requires separately an interpreter for their representation. The authors have developed a knowledge representation form called DCKR (Definite Clause Knowledge Representation) [Koyama 85]. In DCKR, each of the ~i~%~ composing of a Structured Object (hereinafter simply called an ~hJ~Gi) is represented by a Horn clause (a Prolog statement) with the "sem" predicate (to be explained in Section 2) as its head. Therefore, an Object can he regarded as a set of Horn clauses (slots) headed by the sem predicate with the same first argument. From the foregoing it follows that almost all of a program for performing semantic intepretations relative to lexical items described in DCKR can be replaced by functions built in Prolog. That is, most of programming efforts of semantic processing can be left to the functions built in Prolog. DCKR will be described in detail in Section 2. Section 3 will discuss applications of DCKR to semantic processing of natural languages. 2. Knowledge Representation in DCKR The following examples of knowledge representation in DCKR will be used in Section 3 and later. :-op(lO0,yfx,'~'), op(100,yfx,':'), op(90,xfy,'#'). 01) sem(clyde#t,age:6,_). 02) sem(clyde#1,P,S) :- isa(elephant,P,[clyde#11S]). 03) sem(elephant#1,birthYear:lg80, ). 04) sem(elephant#1,P,S) :- isa(elephant,P,[elephant#1:S]). 05) sem(elephant,P,S) :- isa(mammal,P,[elephantlS]). 06) sem(mammal,bloodTemp:warm,). 07) sem(mammal,P,S) :- isa(animal,P,[mammallS]). 08) sem(animal,P,S) :- isa(oreature,P,[animallS]); hasa(faee,P,[animallS]); hasa(body,P,[animallS]). 09) sem(animal,age:X, ) :- bottomof(S,B), sem(B,birthYear:Y,_), X is 1986 - Y. i0) sem(face,P,S) :- hasa(eye,P,[facelS]); hasa(nose,P,[facelS]); hasa(mouth,P,[facelS]). Now the meanings of the sem, i~ and h~a predicates, which are important to descriptions in DCKR, are explained later using the DCKR examples given above. The first argument in the sem predicate is the Qhl~! ngm~. Objects are broadly divided into two types, in~iEi~i~ and RE~!Q%X~. Psychologists often refer to prototypes as stereotypes. An Object name with # represents an in~%~i~i n~ and the one without #, a ~K~!~!Z~ n~. For example, clyde#1 and elephant, which appears in 01l and 05), represent an individual name and a prototype name, respectively. A set of Horn clauses, headed by the sem predicate with the same individual name or prototype name represents an …

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