This paper describes a computer system for understanding English. The system answers questions, executes commands, and accepts information in an interactive English dialog. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language—syntax, semantics, and inference. The system contains a parser, a recognition grammar of English, programs for semantic analysis, and a general problem solving system. We assume that a computer cannot deal reasonably with language unless it can understand the subject it is discussing. Therefore, the program is given a detailed model of a particular domain. In addition, the system has a simple model of its own mentality. It can remember and discuss its plans and actions as well as carrying them out. It enters into a dialog with a person, responding to English sentences with actions and English replies, asking for clarification when its heuristic programs cannot understand a sentence through the use of syntactic, semantic, contextual, and physical knowledge. Knowledge in the system is represented in the form of procedures, rather than tables of rules or lists of patterns. By developing special procedural representations for syntax, semantics, and inference, we gain flexibility and power. Since each piece of knowledge can be a procedure, it can call directly on any other piece of knowledge in the system.

Understanding Natural Language

The main purpose of the research reported here is to show that a new and more powerful type of computer-assisted instruction (CAI), based on extensive application of artificial-intelligence (AI) techniques, is feasible, and to demonstrate some of its major capabilities. A set of computer programs was written and given the name SCHOLAR. Due to its complexity, only the conception and educational aspects of this system (including an actual on-line protocol) are presented in this paper. In what may be called conventional ad hoc-frame-oriented (AFO) CAI, the data base consists of many "frames" of specific pieces of text, questions, and anticipated answers entered in advance by the teacher. By contrast, an information-structure-oriented (ISO) CAI system is based on the utilization of an information network of facts, concepts, and procedures; it can generate text, questions, and corresponding answers. Because an ISO CAI system can also utilize its information network to answer questions formulated by the student, a mixed-initiative dialogue between student and computer is possible with questions and answers from both sides.

AI in CAI: An Artificial-Intelligence Approach to Computer-Assisted Instruction

Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.

/pdf/query-optimization-in-database-systems-502cijfvvq.pdf

Query Optimization in Database Systems

PRUF—an acronym for Possibilistic Relational Universal Fuzzy—is a meaning representation language for natural languages which departs from the conventional approaches to the theory of meaning in several important respects. First, a basic assumption underlying PRUF is that the imprecision that is intrinsic in natural languages is, for the most part, possibilistic rather than probabilistic in nature. Thus, a proposition such as “Richard is tall” translates in PRUF into a possibility distribution of the variable Height (Richard), which associates with each value of the variable a number in the interval [0,11 representing the possibility that Height (Richard) could assume the value in question. More generally, a proposition, p , translates into a procedure, P, which returns a possibility distribution, Π p , with P and Π p representing, respectively, the meaning of p and the information conveyed by p . In this sense, the concept of a possibility distribution replaces that of truth as a foundation for the representation of meaning in natural languages. Second, the logic underlying PRUF is not a two-valued or multivalued logic, but a fuzzy logic, FL, in which the truth-values are linguistic, that is, are of the form true, not true, very true, more or less true, not very true , etc., with each such truth-value representing a fuzzy subset of the unit interval. The truth-value of a proposition is defined as its compatibility with a reference proposition, so that given two propositions p and r, one can compute the truth of p relative to r . Third, the quantifiers in PRUF—like the truth-values—are allowed to be linguistic, i.e. may be expressed as most, many, few, some, not very many, almost all, etc. Based on the concept of the cardinality of a fuzzy set, such quantifiers are given a concrete interpretation which makes it possible to translate into PRUF propositions exemplified by “Many tall men are much taller than most men,” “All tall women are blonde is not very true,” etc. The translation rules in PRUF are of four basic types: Type I—pertaining to modification; Type II—pertaining to composition; Type III—pertaining to quantification; and Type IV—pertaining to qualification and, in particular, to truth qualification, probability qualification and possibility qualification. The concepts of semantic equivalence and semantic entailment in PRUF provide a basis for question-answering and inference from fuzzy premises. In addition to serving as a foundation for approximate reasoning, PRUF may be employed as a language for the representation of imprecise knowledge and as a means of precisiation of fuzzy propositions expressed in a natural language.

PRUF—a meaning representation language for natural languages

An information server directs users of the information server to desired sources of information where the desired sources of information are determined, at least in part, based on user input. The information server includes a query input processor, a question processor and an answer processor. The query input processor is used for accepting an initial user query. The question processor processes the initial user query to identify a set of possible well-formed questions selected from the question database, where a well-formed question is a question in the database that is coupled to at least one answer reference. The answer reference is typically either an answer or a pointer to a possible location of an answer. In a specific embodiment, the information server is coupled to the Internet so that users can pose questions using a Web browser from any Internet-connected device. In some systems, the question processor includes a tokenizer for tokenizing the initial user query into a list of words, a parser for generating a syntactic structure from the list of words, a normalizer for reducing the syntactic structure to a canonical syntactic structure, and a matcher for matching the canonical syntactic structure against a semantic network to obtain a weighted list of well-formed questions representative of possible semantic meanings for the initial user query.

Grammar template query system

Inference planning techniques have been implemented and incorporated within a prototype deductive processor designed to support the extraction of information implied by, but not explicitly included in, the contents of a relationally structured data base, Deductive pathfinding and inference planning are used to select small sets of relevant premises and to construct skeletal derivations. When these “skeletons” are verified, the system uses them as plans to create data-base access strategies that guide the retrieval of data values, to assemble answers to user requests, and to produce proofs supporting those answers. Several examples are presented to illustrate the current capability of the prototype Deductively Augmented Data Management (DADM) system.

Deductive planning and pathfinding for relational data bases

During the past few years there has been a rapid advance in the technology of time-sharing systems and software to permit quick access to large files of structured data. This has led to a growing interest in communicating with computer files directly in a natural language such as English. The natural language systems described in the literature are largely small-scale research vehicles dealing with small data bases of restricted subject scope. Giuliano (1965), among others, has questioned the generalization of these systems to wider universes of discourse. Developments in this area have been reviewed by Simmons (1966), and by Bobrow, Fraser and Quillan (1967). In contrast, the work in on-line data management has been more concerned with the efficient organization of structured data to allow for quick access and maintenance of large volumes of formatted information [see the reviews by Kellogg (1967), Climenson (1966), and Minker and Sable (1967)].

A natural language compiler for on-line data management

This paper describes the central features of a system designed for the management of large amounts of application specific knowledge. The Knowledge Manager(KM-I) employs distinct software and hardware processors to implement:

A file of general knowledge and an associated reasoning engine

A file of specific knowledge and an associated searching engine

We present our reasons for believing why this can be an effective strategy for realizing many practical knowledge based/expert system applications that lie in a large overlapping area between practical AI and advanced data management technology. We then outline the major features and components of the system and discuss the range of intended applications.

Knowledge management: a practical amalgam of knowledge and data base technology

A system for applying the theory of logical deduction and proof procedures to the accessing of data stored in conventional data management systems is described and illustrated with several examples. The DADM (Deductively Augmented Data Management) system has been developed along several dimensions of utility and performance to provide a vehicle for research on interactive techniques for reasoning with data, answering questions, and supporting on-line decision making. After illustrating present system operation by means of several examples, new performance-enhancing features of the system are described. These features include improved user interfaces, improved visibility of processes and data structures, structure sharing, improvements in inference-planning mechanisms, methods for dealing with incomplete information, utilization of semantic advice, and means for controlling recursive premises.

Reasoning with Data in a Deductively Augmented Data Management System

Work on integrating systems capable of drawing inferences from knowledge b,wes containing large numbers of logical clauses with relational datab,we systems containing large numbers of facts is described The aim is to realize the derivational power of symbolic logic while at the same time exploiting the set-processing capabilities and potential parallelism of relational data base systems We propose that the interface between the deduction and database components involve set-characterizing relational algebra programs (RAPS) and sets of answer values, rather than proceeding sequentially with single answer tuples being requested and returned from the database system one by one Our design includes a query compiler that translales queries into RAPS, as well as a rule compiler that compiles rules into an elliciently maintainable and incrementally updateable predicate connection graph (PCG), a structure whose use obviates open ended deductive search at query time When prewnted with a query, the system extracts from the PCG a proof schema that represents all possible derivations of the query from the +afional database Structure sharing within this proof schema provides a b,asis for producing from the schema a significantly optimized RAP Direct manipulation of the RAP expression enables fur&r optimization, and the optimized program is then evahmtcd against the database The result is a set of all possible answers to the rluery, produced with minimal scar& of the database Answers may then be combined with certain intermediate rem]Ls and proof schcmn inlormation to gencrate explanations describing how the answers were derived from the knowledge base We describe a prototype implementation of this proposed design and report on preliminary empirical explorations Some results or the explorations are lhat, although the number of derivations represented in a proof schema grows log exponentially with respect to deductive complexity (in one example the number approaches three million), RN’ size appears to grow only linearly with deduc-

Optimizing the Rule-Data Interface in a KMS

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