Showing papers on "Knowledge extraction published in 1988"

Task-related knowledge structures: analysis, modelling and application

Abstract: A theoretical and methodological approach to task modelling is described, with a worked example of the resultant model. The theory holds that task knowledge is represented in a person's memory and that this knowledge can be described by a Task Knowledge Structure (TKS). The method of analysis has been developed for carrying out analyses of real world tasks. The method uses a variety of techniques for collecting information about task knowledge. A second perspective of the paper shows how a developed TKS model can be decomposed into a design for a software system to support the identified tasks within the domain of the analysis. This decompositional method uses the structure of frames to provide consistency between different levels of design decomposition.

Automated concept acquisition in noisy environments

Abstract: A system that performs automated concept acquisition from examples and has been specially designed to work in noisy environments is presented. The learning methodology is aimed at the target problem of finding discriminant descriptions of a given set of concepts and uses both examples and counterexamples. The learned knowledge is expressed in the form of production rules, organized into separate clusters, linked together in a graph structure. Knowledge extraction is guided by a top-down control strategy, through a process of specialization. The system also utilizes a technique of problem reduction to contain the computational complexity. Several criteria are proposed for evaluating the acquired knowledge. The methodology has been tested on a problem in the field of speech recognition and the experimental results obtained are reported and discussed. >

Dissertation abstract

Abstract: Information retrieval can be defined as the extraction of specific information out of a great number of stored information items. Information retrieval systems, used for the retrieval of documents, try to answer more or less precise questions about interesting topics with a number of suitable documents or references to documents. Such systems should contain 'knowledge' about the meaning of questions, about the content of the stored information and the particular user's needs for information.Knowledge-bases systems claim to be able to store knowledge an draw conclusions from it. The goal of this thesis is to investigate the use of knowledge-based methods and technologies for information retrieval. A knowledge-based information retrieval system should represent its Information Structures, as well as knowledge in a common knowledge representation formalism. The retrieval process of the system should employ the inferential methods of the used knowledge representation formalism.A subset of first order logic is chosen for this thesis to represent knowledge. Specially designed retrieval rules represent knowledge for the purpose of retrieval. Retrieval rules capture knowledge about the user's vocabulary, his working domain and his way to perform the retrieval of documents. The problem of recall and precision of the answers of an information retrieval system is approached by an explicit representation of control knowledge.A theoretical model of a knowledge-based information retrieval system developed in this thesis specifies the requirements and properties, of such a system. In particular a novel term-similarity function could be defined. Properties like completeness and termination could be derived and boundaries for the amount of overhead of false control strategies could be investigated.The proposed model is implemented in a prototype of a knowledge-based information retrieval system, called KIR. KIR is a single-user system for personal document- and knowledge retrieval running on computer workstations. It is implemented using Prolog and Modula-2.

Uses of repertory grid-centered knowledge acquisition tools for knowledge-based systems

Abstract: Repertory grid-centred knowledge acquisition tools are useful as knowledge engineering aids when building many kinds of complex knowledge-based systems. These systems help in rapid prototyping and knowledge base analysis, refinement, testing, and delivery. These tools, however, are also being used as more general knowledge-based decision aids. Such features as the ability to very rapidly prototype knowledge bases for one-shot decisions and quickly combine and weigh various sources of knowledge, make these tools valuable outside of the traditional knowledge engineering process. This paper discusses the use of repertory grid-centred tools such as the Expertise Transfer System (ETS), AQUINAS, KITTEN, and KSSO. Dimensions of use are presented along with specific applications. Many of these dimensions are discussed within the context of ETS and AQUINAS applications at Boeing.

Knowledge-based simulation model specification

Abstract: A Knowledge-Based Model Construction (KBMC) system has been developed to automate the model construction phase of the simulation life cycle. The system's underlying rule base in corporates several types of knowledge. This includes domain knowledge that facilitates a structured interactive dialog for the acquisition of a complete model specification from a user. An executable discrete simulation model in SIMAN is automatically constructed by the system from this specification, utilizing model ing knowledge and SIMAN knowledge. We present an overview of the KBMC system and focus on the knowledge-based specifica tion method used in the KBMC system.

A knowledge-based approach for automated process planning

Abstract: Automated process planning (APP) is a critical interface to both design and manufacturing. Several evolving systems have employed artificial intelligence (AI) procedures to capture the basic logic used by a process planner. However, no effort has been directed to systemize the knowledge in the field of process planning. In this paper process planning knowledge extraction and formalization will be discussed. A framework for the development of intelligent APP systems capable of learning through user interaction is also presented.

Technique for compilation of knowledge bases

Abstract: A method is disclosed for converting both a knowledge base and an inferencing technique into compilable program code forming a knowledge based system. The method is based on determining what actions an interpretitive inference engine would take with the specific knowledge base and generating only the program code needed to perform these actions. These method eliminates the overhead of interpreting a representation of the knowledge base and significantly improves performance of the system. The method is applicable to both forward and backward chaining reasoning strategies and does not modify or restrict the functional capabilities of the knowledge based system.

Support for knowledge acquisition in the Knowledge Engineer's Assistant (KEATS)

Abstract: The ‘Knowledge Engineer's Assistant’ (KEATS) is a software environment suitable for constructing knowledge-based systems. In this paper, we discuss its role in supporting the knowledge engineer in the tasks of knowledge elicitation and domain understanding. KEATS is based upon our own investigations of the behaviour and needs of knowledge engineers and provides two enhancements to other modern ‘shells’. ‘toolkits’, and ‘environments’ for knowledge engineering: (i) transcript analysis facilities, and (ii) a sketchpad on which the KE may draw a freehand representation of the domain, from which code is automatically generated. KEATS uses a hybrid representation formalism that includes a frame-based language and a rule interpreter. We describe the novel components of KEATS in detail, and present an example of how KEATS was used to build an electronic fault diagnosis system.

Knowledge-based decision support in business: Issues and a solution

Abstract: The authors describe an implemented system supporting the maintenance of evolving symbolic models and the spreadsheet-like algebraic models based on them. The system's primitives are knowledge fragments that are instantiated into symbolic models; these fragments can be modified in response to changes in the task environment, and appropriate changes induced in the algebraic model to reflect changes in the symbolic model. They analyze model features for problem-solving in domains with three levels of structuredness: highly formalized problems. The authors define data structures used for representing choice sets and constraints and describe the system's procedural knowledge component, which allows knowledge represented in the structures to be used in an integrated way. >

Knowledge base management method and system

Abstract: A knowledge base management method and system provide a knowledge base file, a system control processor, a plurality of knowledge representation analyzers provided individually for knowledge representing languages or programming languages and independently of the system control processor, and a plurality of knowledge information catalogs provided individually for the programming languages and independently of the system control processor. The knowledge representation analyzer analyzes a module of knowledge inputted thereto to extract information concerning a relation (for example, relation in inheritance) between the inputted module of knowledge and another module of knowledge. The knowledge information catalog stores the information obtained through the analysis by the knowledge representation analyzer with relation to a storage location of the corresponding module of knowledge in the knowledge base file. When one module of knowledge described by any given programming language is to be stored into the knowledge base file, the module of knowledge is analyzed by one of the knowledge representation analyzers corresponding to the given programming language and information extracted from the module of knowledge by the analysis thereof is stored into one of the knowledge information catalogs corresponding to the given programming language.

An AI-Based Approach for the Integration of Multiple Sources of Knowledge to Aid Engineering Design

Abstract: A model for integrating multiple sources of knowledge within engineering expert systems is presented. It allows possible conflicts between multiple knowledge sources to be logically resolved at run-time rather than during the knowledge acquisition stage. Unlike the traditional approach in which the knowledge engineer is responsible for resolving conflicting views, resolutions are dynamically accomplished by the knowledge sources themselves and/or by system users. The system user is included as a problem-solving colleague to select a proper strategy from those offered by different experts. Both qualitative and quantitative constraints are traced during problem solving and can be retracted if necessary. The model has been successfully implemented in an engineering design domain to demonstrate the basic ideas. This research is our first step in a long-term effort to develop a cooperative problem-solving paradigm for knowledge-based engineering systems.

Knowledge acquisition as knowledge assimilation

Abstract: The assimilation of information obtained from domain experts into an existing knowledge base is an important facet of the knowledge acquisition process. Knowledge assimilation requires an understanding of how the new information corresponds to that already contained in the knowledge base and how this existing information must be modified so as to reflect the expert's view of the domain. This paper describes a system, K n A c, that modifies an existing knowledge base through a discourse with a domain expert. Using heuristic knowledge about the knowledge acquisition process, K n A c anticipates modifications to existing entity descriptions. These anticipated modifications, or expectations , provide a context in which to assimilate new domain information.

A structured knowledge elicitation methodology for building expert systems

Abstract: A key problem in building expert systems is the extraction of knowledge from human experts. This paper presents a conceptual framework and methodology for knowledge elicitation. The framework models, in a domain-independent manner, the structure of human problem solving knowledge and the context in which problems are solved. It defines the knowledge that should be elicited by the methodology and helps derive the procedure used by the methodology to extract knowledge. This framework is used to develop a structured multi-phase methododo-logy that elicits knowledge in a domain independent manner. This methodology is partially implemented as a computer program in Turbo—Pascal and was used to elicit knowledge from experts in a sample real-world setting. Reliability and validity evaluations performed on the elicited knowledge establish the validity of this approach.

Acquiring strategic knowledge from experts

Abstract: This paper presents an approach to the problem of acquiring strategic knowledge from experts. Strategic knowledge is used to decide what course of action to take, when there are confiicting criteria to satisfy and the effects of actions are not known in advance. We show how strategic knowledge challenges the current approaches to knowledge acquisition: knowledge engineering, interactive tools for experts, and machine learning. We present a knowledge acquisition methodology embodied by an interactive tooi that draws from each approach, automating much of what is currently performed by knowledge engineers, and synthesizing interactive and automatic learning techniques. The technique for eliciting strategic knowledge from experts and transforming it into an executable form addresses the technical problems of operationalization, encoding examples, biasing generalization, and the new terms problem.

Information and Meta Information

Abstract: A model of information is presented, in which statements such as "the information sets are common knowledge" may be formally stated and proved. The model can also abe extended to include the statement: "this model is common knowledge" in a well-defined manner, using the fact that when an event A is common knowledge, it is common knowledge that A is common knowledge. Finally, the model may also be used to define a "natural" topology on information.

Knowledge-based approach to requirements engineering using method and domain knowledge

Abstract: Within information systems development the correct capture of user requirements plays a central role in the construction of effective and flexible systems. This paper views requirements specification as primarily a knowledge intensive activity, and the work reported here follows the premise that the next generation of requirements engineering environments will be knowledge-based. The paper argues that these environments will need to make use of two types of 'requirements analysis' knowledge, namely method and domain knowledge. Method knowledge relates to the design discipline adopted by a systems analyst in order to develop a requirements specification. Much of this knowledge relates to the formalism underlying a particular development approach. The paper discusses how such knowledge may be used to assist analysts, and shows how development methods may be analysed in terms of their conceptual formalisms. It argues that systems analysts make extensive use of domain knowledge when attempting to specify requirements, and analyses the characteristics of this knowledge. A knowledge-based requirements specification environment currently under development is also presented.

Representation of Structural Design Knowledge in a Symbolic Language

Abstract: A structural design language (SDL) is developed in the INTERLISP environment for development of coupled knowledge‐based expert systems for structural design problems. This article presents the representation of the structural design knowledge in SDL. The knowledge necessary for design is classified into three categories: static knowledge, dynamic knowledge, and graphical knowledge. Static knowledge is defined as the knowledge necessary for representing the physical structure, its components, and their topology. Dynamic knowledge includes the knowledge of design constraints that have to be satisfied in a given design problem and the heuristics that are used to solve the problem effectively. Four representation schemes are used for representing the static knowledge; that is, atom, list, array, and object‐attribute‐value triplet. Dynamic knowledge is represented by production rules and functionals via procedural abstraction. Graphical knowledge is represented by bitmaps, windows, and menus.

Information and Meta Information

Abstract: A model of information is presented, in which statements such as "the information sets are common knowledge" may be formally stated and proved. The model can also abe extended to include the statement: "this model is common knowledge" in a well-defined manner, using the fact that when an event A is common knowledge, it is common knowledge that A is common knowledge. Finally, the model may also be used to define a "natural" topology on information.

Knowledge information generating system

Abstract: A knowledge information generating system including: a fundamental rule storage for making and storing, for each process data to be controlled, another process data relating to the former and the relations between these process data in advance as fundamental rules; a process data input processor for inputting and storing the process data to be controlled; a knowledge information collection necessity decider for detecting that the process data obtained from the process data input processor have changed or deviated from a predetermined relation; a knowledge information editor for fetching the fundamental rules relating to the process data, which are decided to be collected by the knowledge information collection necessity decider, from the fundamental rule storage, to edit the process data on the basis of the relations between the data group stored in the process data input processor and the process data stored in the fundamental rules; and a knowledge data base for storing the knowledge information obtained from the knowledge information editor.

Knowledge based systems for speech understanding

Abstract: Based on a detained discussion of system architectures for knowledge based speech understanding and knowledge representation techniques, criteria for both a knowledge representation scheme and system architectures are developed. Upon this background a system is introduced which is organized around a homogenuous knowledge base. Both the knowledge representation language and the content of the knowledge base are described. The knowledge representation language do not only cover declarative but also procedural knowledge. Analysis processes are guided by a flexibel bottom-up top-down strategy. Besides the procedural semantics of the language the A -Algorithm is used for this purpose. Search graph nodes are judged by a vector which reflects knowledge dependent and acoustic scores and which is admissable for the A -Algorithm.

Knowledge representation for model libraries

Abstract: A knowledge-representation scheme is proposed for a library of complex models with an emphasis on knowledge of management-science paradigms. The scheme supports the model type, the model template, and the model instance levels. At the model-type level, reasoning about assumptions, mathematical form, inputs, outputs, and solvers is supported. The library maintains a taxonomy of model types based on these features. At the model template and model instance levels, reasoning about model details and business knowledge incorporated into the model are supported. An overview of the library operators is provided, and the assumption matching operator, which permits inexact searching, is described in detail. >

Knowledge based support for the management of statistical databases

Abstract: It is often indicated that the relational model should be extended to conform with the needs of statistical analyses (GHOSH 1988). The examples demonstrate that so-called ‘relational models’ in common DBMS may in some cases even be to rich. Thus it is not sufficient, to extend a DBMS for commercial applications by statistical operators. In some cases, it may even be necessary to restrict the set of operators. In order to check semantically integrity constraints, a DBMS for statistical applications needs knowledge on the lattice structure of observational units (design knowledge) and on the concepts underlying the observational units and attributes (model knowledge). This type of knowledge has so far been not considered, because it is of little importance for commercial applications. For statistical applications, however, it is necessary to avoid semantically meaningless analyses and to assist the user in performing analyses.

KDL-advisor: a knowledge/data based system written in KDL

Abstract: A novel approach is described for building intelligent information systems (or knowledge-base management systems). The approach utilizes the knowledge data language, which is a schema specification language developed for the knowledge/data model. The model, referred to as a hypersemantic data model, captures both knowledge semantics, as specified in knowledge-based systems, and data semantics, as represented by semantic data models. Hypersemantic data models facilitate the incorporation of knowledge in the form of heuristics, uncertainty, constraints and other artificial intelligence concepts, together with object-oriented concepts found in semantic data models. The unified knowledge/data modeling features and constructs of the language are used to develop a prototype knowledge base management system, the KDL-advisor. >

ASTEK: A multi-paradigm knowledge acquisition tool for complex structured knowledge

Abstract: Knowledge-based systems can require large, highly complex and varied forms of knowledge. An effective knowledge acquisition tool to support such a system should allow the user to transfer and manipulate the different forms knowledge in a manner that is clear and intuitive. ASTEK is a knowledge acquisition tool that provides multiple paradigms for knowledge editing while maintaining a single, consistent framework designed using natural language discourse concepts.

Chapter 45 – Human Factors in Knowledge Acquisition

Abstract: Publisher Summary This chapter focuses on the human factors in knowledge acquisition. An initial need to build an expert system is for the system builders to acquire a good understanding of the domain. There is no substitute for this, and it constitutes the first stage of knowledge acquisition. Similarly, it is important to identify an expert, or a small group of experts who can work with the knowledge acquirers. These experts should come to understand the goals and constraints of a computer system, so they can be helpful in imparting their knowledge. The multiple methods of eliciting and acquiring knowledge should be used, both because knowledge is so complex and because any method should bring with itself characteristic biases and potentials for distortions and gaps. Closely related to this, mixed methods of knowledge acquisition show considerable promise. This means that multiple, integrated techniques for extracting knowledge should be used, which can combine the virtues of different technique.

An object-oriented approach to knowledge systems

Abstract: A method for an object oriented modeling of knowledge systems called DKOM (Distributed Knowledge Object Modeling) is proposed. In this modeling method, a knowledge system consists of cooperative knowledge objects, where each knowledge object consists of a behavior part, a knowledge part, and a monitor part. An object oriented language called ORIENT84/K has been designed based on the DKOM. The behavior part of an object contains methods like those in Smalltalk; the knowledge part contains rules and facts like those in Prolog; and the monitor part monitors and controls the object. The relation between class and object, the relation between the behavior part and knowledge part, inference from knowledge, addition and deletion of knowledge, addition and deletion of methods, and access control of objects are described. An expert system is built using ORIENT84/K and the performance of ORIENT84/K is compared with some other programming languages/systems.

Entity-Situation: A Model for the Knowledge Representation Module of a KBMS

Abstract: ADKS is an advanced data and management system whose main objective is to couple expressiveness and efficiency in the management of large knowledge bases is described. The architecture of the system and a new semantic model which is the basis of its knowledge representation module is presented.

DALI-a knowledge base management system

Abstract: This paper propagates the centralization of the data management functions of large computerized knowledge bases in a module called Knowledge Base Management System(KBMS) and discusses the advantages and disadvantages of this approach. Furthermore, it surveys the concepts and implementation of a KBMS called DALI. DALI uses a semantic net like knowledge representation approach. A DALI knowledge base contains sets of entities. Entities are members of classes. Classes are either of the type data, rule or operation, representing data objects, rules or operations respectively. Our data model supports data driven as well as command driven control regimes. Rule entities are triggered by data changes of the knowledge base. Operation entities can be compiled, loaded, uncompiled, applied, created, changed and deleted. It is possible to assign interface specifications to operation entities, which are automatically checked by the KBMS when the operation is applied. Constraints can be imposed on the membership of entities to classes. DALI uses an optimistic consistency enforcement approach, which tolerates but controls inconsistencies in the knowledge base. Furthermore, DALI conforms to the principle of information hiding.