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The Use of a Semantic Network in a Deductive Question-Answering System

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The Use of a Semantic Network in a Deductive Question-Answering System THE USE OF A SEMANTIC NETWORK IN A DEDUCTIVE QUESTION-ANSWERING SYSTEM James R. McSkimin .Jack Minker Bell Telephone Laboratories Department of Computer Science Columbus, Ohio University of Maryland 43209 College Park, Maryland 20742 Abstract tions. Thus, it should be possible to deter• mine that a query such as "Who is the person The use of a semantic network to aid in the who is both father and the mother of a given deductive search process of a Question-Answering individual?n is not answerable. System is described. The semantic network is (3) To identify those queries which have a known based on an adaptation of the predicate calculus. maximum number of solutions so as to termi• It makes available user-supplied, domain-dependent nate searches for additional answers once the info mat ion so as to permit semantic data to be known fixed number is found. used during the search process. The semantic network discussed in this paper Three ways are discussed in which semantic is described briefly in Section 2. In Section 3, information may be used. These are: we describe how the semantic network may be used (a) To apply semantic information during the to solve some of the problems associated with the pattern-matching process. above items. An example which illustrates the (b) To apply semantic well-formedness tests to use of the semantic network is presented in query and data inputs. Section 4. A summary of the work and future (c) To determine when subproblems are fully- directions is given in Section 5. solved (i.e., they have no solutions other than a fixed, finite number). 2. Seniantjc Network An example is provided which illustrates the Although the term 'semantic network' has been use of a semantic network to perform each of the used extensively in the literature, there is no above functions. universal agreement as to what constitutes such a network. Hence, we shall define the term in the 1. Introduction context of this paper. Semantic Networks have been used primarily The semantic network to be described arose in natural language applications to help disam• out of the need to provide meaning to objects in biguate sentences and to understand natural a domain and to statements made about these ob• language text. In this paper we consider the use jects so as to make deductive searches more ef• of a semantic network to aid in the deductive ficient. Although the semantic network developed search process of a Question-Answering (QA Sys• is used in deductive searches, it nevertheless tem. The semantic network is based on an adapta• bears considerable relationship to those tion of the predicate calculus and is described developed through the need to understand natural only briefly in this paper and more extensively language by computers. The semantic network by McSkimin and Minker (McSkimin [19761, and developed by Schubert [1976], for example, bears McSkimin and Minker [1977]). Terminology from many similarities to the one used here. the predicate calculus will be used throughout the paper. The semantic network described here is an adaptation of the predicate calculus and is able Three ways will be discussed in which seman• to express quantification, functions, terms and tic information may be applied to help restrict logical connectives. The adaptation is based deductive searches. These are: upon the notation of Fisimian and Minker (Fishman (1) To apply semantic information during the [1973], Fishman and Minker [1975]), who modified pattern-matching process (unification al- predicate calculus clause notation to handle sets gorithin). Most current pat tern-matching sys• of objects that have the same template structure. tems are based solely on syntactic tests. Using the semantic network, semantic con• Thje article by Schubert [1976] discusses straints may be applied during the pattern- many semantic network representations used for matching process to inhibit data base asser• natural language processing, and surveys the tions and general axioms that are semantical!}' literature so that we neither refer to nor com• irrelevant to the search from entering into pare our work on semantic networks with that the deductive search space. achieved by others. (2J To apply semantic well-formedness tests to query and data base assertions input to the In order to implement the techniques des• system so as to reject queries that have no cribed in the introduction, domain-dependent answer because they violate semantic restric- information must be stored in the computer in a form convenient for use. Coasequently, a major Matural Lanrnj age-3 : McSk iMin 50 part of this research has concerned the identifi• necessary to subdivide the domain D into subsets cation of the types of semantic information to be since certain relational statements may only be stored, and the development of structures in which made about specified subsets of D, and one would to store the information. To this end, a collec• like to make these subsets explicit rather than tion of structures termed the "semantic network" implicit. This subdivision is specified by a has been developed which contains all information semantic graph Gs which defines how each category available to the question-answering system. C is subdivided into subsets C1,C2,...,Cn and how The semantic, network consists of four com• each of the Cj is similarly defined. Figure 1 ponents: (1) the semantic graph which specifies shows an example of such a graph. Note that both the set-theoretic relation between named subsets animate and living are the superset of animal; of the domain; (2) the data base of assertions however animate is the superset of robot which is and inference rules; (37 the semantic form space disjoint from Living, and living is the superset which defines the semantic constraints placed on of plant which is disjoint from animate. Thus, arguments of relational n-tuples; and (4) the animate and living overlap. dictionary which defines the set membership for Subdividing D in this manner and defining each element of the domain. All four components where in the hierarchy each domain element lives of the semantic network are used by the techniques (the function of the dictionary), has several ad• described above for making the QA process more vantages over expressing set memberships by unary efficient. Illustrations of how this information relations. In particular, it should be computa• is used will be given in the next section. tionally more efficient to perform trivial set (a} The Semantic Graph membership inference using such a structure rather than by using unary relations. Thus, Sirica £ The major emphasis of this effort is the judge might be stored in the dictionary rather investigation of techniques by which user-supplied than storing the unit clause JUDGE(Sirica) semantic information may be stored in a computer in the data base. The rationale for this choice and used to make the deductive inference process is given in McSkimin [1976]. more efficient. The approach taken is to define explicitly the' contents of the domain of discourse (b) Data Base D as well as the relationships in which various Assertions are facts, whereas general axioms subsets of the domain may occur. are used to infer assertions about domain elements To this end, much of the work has involved that are otherwise stored implicitly in the data the investigation of how one might subdivide base. Both types are stored in a "parallel clause" the domain D into a finite number of named subsets notation, termed n-o notation, an extension of the Sj such that all elements of each S^ have some set n-representation of Fishman and Minker. An exam• of properties in common. These sets are expressed ple of an assertion in n-o notation is: ((a,x,y), as Boolean Category Expressions (BCE). Examples {{ [PARENT]/a,[Ruth,Herb]/x, lAnne,Carol,Jim]/y}}). are: senatord male -liberal, state, judge f] The assertion states that Ruth and Herb are the lawyer. The names "senator", "state" and "judge" parents of Anne, Carol and Jim. An example of a are examples of what are defined to be the sim• general axiom is: (^(a,x, y.) v (3, x, y), {{ [RES IDE]/a, plest type of BCE possible and are called semantic congressperson/x,state/y,[REPRESENT]/B)3). The categories. A BCE is any arbitrary combination axiom states that for all «, 3, x, and y, if the of categories using the set operations of union object x is in the set congressperson, and the ob- (ll), intersection (n) and complement (-). It is ject a is the predicate" RESIDE, and x residei s in and the two II-a literals would be prevented from with semantics because at least one of its argu• unifying. Thus, clause (3) would never be entered ments conflicts with the corresponding argument of into the search space, so that it would not lead all semantic forms for that n-tuple size. to a deductive search path, thereby decreasing the time and space used over that of a purely syntac• Although some instances of the n-o literal L tic pattern match. might fail to unify with any semantic form, others may succeed. What is desirable therefore, is to Semantic unification is applied during the transform a II-o clause input to the system into deductive search process. It is also applied when one (or perhaps several) clauses that are entirely one is entering new facts or general rules into well-formed. These clauses may then be entered the system, and when a query is entered. These into the data base or input to the deductive are described in the following sections. mechanism as appropriate. Those instances failing 5.2 Semantic Well-Formedness of n-o Clauses to unify should be isolated and the user informed of the error.
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