Showing papers on "Knowledge extraction published in 1987"

Knowledge base verification

Abstract: Publisher Summary This chapter describes several tools and techniques for validating a knowledge base, with emphasis on the rule checker. The other tools, such as the syntax checker and test case manager, also help the knowledge engineer to maintain the correctness of one's knowledge base and of the tool one is using. From the experiences with constructing different knowledge bases, one fined that many changes and additions are made during the development and maintenance of a knowledge base. The most frequent problems that CHECK has detected are unreachable and dead-end clauses. These types of errors are difficult to detect with conventional knowledge base debugging aids. It is found that redundant and conflicting rules appeared the least often. Nevertheless, CHECK'S automated facilities have saved engineers from many hours of tedious debugging. As the field of knowledge-based systems matures, large expert systems will be fielded in critical situations. As it will be impossible to test all paths beforehand, one must have assurance that deadly traps, such as circular rules and dead-end clauses do not exist in the knowledge base. Thus, verification facilities similar to the ones described in this paper becomes essential.

Knowledge representation for model management systems

Abstract: This paper examines the concept of a model management system, what its functions are, and how they are to be achieved in a decision support context. The central issue is model representation which involves knowledge representation and knowledge management within a database environment. The model abstraction structure is introduced as a vehicle for model representation which supports both heuristic and deterministic inferencing as well as the conceptual/external schema notion familiar to database management. The model abstraction is seen as a special instance of the frame construct in artificial intelligence. Model management systems are characterized as frame-systems and a database implementation of this approach is described.

Method for integrating a knowledge-based system with an arbitrary database system

Abstract: A method and system are provided for mapping between an application relational database of arbitrary structure and an application knowledge base in order to permit a user to draw inference through a knowledge base. Also included are procedures for translating knowledge base queries into database queries, for transforming data retrieved from the database into units (structured objects) in the knowledge base and for updating a relational database based on changes made to the application knowledge base. These procedures are supported by general purpose knowledge bases. The method includes providing mapping knowledge bases for storing the mapping between an arbitrary relational database and the application knowledge base. The mapping between classes in the application knowledge base and the relations on a database is stored explicitly in units in a user mapping knowledge base. These units are called class maps. The mapping between the slots of a class in an application knowledge base and the attributes of the above relations on the database is stored explicitly in a slot of the class map corresponding to the class.

Use of a domain model to drive an interactive knowledge-editing tool

Abstract: The manner in which a knowledge-acquisition tool displays the contents of a knowledge base affects the way users interact with the system. Previous tools have incorporated semantics that allow knowledge to be edited in terms of either the structural representation of the knowledge or the problem-solving method in which that knowledge is ultimately used. A more effective paradigm may be to use the semantics of the application domain itself to govern access to an expert system's knowledge base. This approach has been explored in a program called OPAL, which allows medical specialists working alone to enter and review cancer treatment plans for use by an expert system called ONCOCIN. Knowledge-acquisition tools based on strong domain models should be useful in application areas whose structure is well understood and for which there is a need for repetitive knowledge entry.

Inference in text understanding

Abstract: The problem of deciding what was implied by a written text, of "reading between the lines" is the problem of inference. To extract proper inferences from a text requires a great deal of general knowledge on the part of the reader. Past approaches have often postulated an algorithm tuned to process a particular kind of knowledge structure (such as a script, or a plan). An alternative, unified approach is proposed. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures, but instead rely on patterns of connectivity between concepts. The complexity has been effectively shifted from the algorithm to the knowledge base; new kinds of knowledge structures can be added without modifying the algorithm.

KRITON: a knowledge-acquisition tool for expert systems

Abstract: A hybrid system for automatic knowledge acquisition for expert systems is presented. The system integrates artificial intelligence and cognitive science methods to construct knowledge bases employing different knowledge representation formalisms. For the elicitation of human declarative knowledge, the tool contains automated interview methods. The acquisition of human procedural knowledge is achieved by protocol analysis techniques. Textbook knowledge is captured by incremental text analysis. The goal structure of the knowledge elicitation methods is an intermediate knowledge-representation language on which frame, rule and constraint generators operate to build up the final knowledge bases. The intermediate knowledge representation level regulates and restricts the employment of the knowledge elicitation methods. Incomplete knowledge is laid open by patterndirected invocation methods (the intermediate knowledge base watcher) triggering the elicitation methods to supplement the necessary knowledge.

Design for acquisition: principles of knowledge-system design to facilitate knowledge acquisition

Abstract: The problem of knowledge acquisition is viewed in terms of the incongruity between the representational formalisms provided by an implementation (e.g. production rules) and the formulation of problem-solving knowledge by experts. The thesis of this paper is that knowledge systems can be designed to facilitate knowledge acquisition by reducing representation mismatch. Principles of design for acquisition are presented and applied in the design of an architecture for a medical expert system called MUM. It is shown how the design of MUM makes it possible to acquire two kinds of knowledge that are traditionally difficult to acquire from experts: knowledge about evidential combination and knowledge about control. Practical implications for building knowledge-acquisition tools are discussed.

Knowledge systems and PROLOG

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KNACK—report-driven knowledge acquisition

Abstract: This paper describes a knowledge-acquisition tool that builds expert systems for evaluating designs of electro-mechanical systems. The tool elicits from experts (1) knowledge in the form of a skeletal report, (2) knowledge about a large collection of report fragments, only some of which will be relevant to any specific report, and (3) knowledge of how to customize the report fragments for a particular application. The tool derives its power from exploiting its understanding of two problem-solving methods and of the different roles that knowledge plays in those two methods.†

Knowledge management in decision support systems

Abstract: This paper discusses a framework for knowledge management in a DSS. We assume decision making is based mainly on numerical data processing. Thus, we abstract data and knowledge as relations, and decision models as relators. Based on these two constructs, our framework allows a user to compose and experiment decision models interactively; it also provides decision information nonprocedurally through a knowledge processor.

A conceptual framework for knowledge elicitation

Abstract: This paper includes a statement of the knowledge-elicitation problem and argues that the problem is two-fold: to outline a conceptual framework and to develop and validate a knowledge extraction methodology. Required and desirable attributes of a knowledge elicitation methodology are discussed. A conceptual framework that may be used to derive a knowledge elicitation methodology is outlined. This conceptual framework is established by extending Newell’s and Simon's (1972) problem space concept and integrating it with Kelly's (1955) theory of personal constructs. This framework provides guidelines regarding the kind of knowledge to be elicited, and the sequence and format in which this should be done. It also elicits knowledge that is used subconsciously and in unique ways by the expert.

Knowledge-based knowledge acquisition for a statistical consulting system

Abstract: Knowledge-based knowledge acquisition means restricting the domain of knowledge that can be acquired and developing a conceptual model of the domain. We have built a prototype knowledge-based knowledge acquisition system for the domain of data analysis. A critique of the prototype has led to a design for a possibly practical data analysis knowledge acquisition system.

Lending Analysis Support System (LASS): An Application of a Knowledge-Based System to Support Commercial Loan Analysis

Abstract: Knowledge-based systems represent one of the most important and fastest growing research topics in the field of information and decision sciences. They promise to have an appreciable impact on decisionmaking in ill-structured decision settings where managers often employ semilogical procedures and tacit knowledge. The incorporation of knowledge engineering concepts with decision-support systems is discussed. A knowledge-based system supports the selection of appropriate models and data which are features contained in a conventional decisionsupport system. A knowledge-based prototype designed for the difficult problems encountered by commercial loan officers is also described, as are the elicitation and representation techniques employed in developing the prototype.

INFORM: an architecture for expert-directed knowledge acquisition

Abstract: This paper presents an architecture for INFORM, a domain-independent, expert-directed knowledge acquisition aid for developing knowledge-based systems. The INFORM architecture is based on information requirements and modeling approaches derived from both decision analysis and knowledge engineering. It emphasizes accommodating cycles of creative and analytic modeling activity and the assessment and representation of aggregates of information to holistically represent domain expertise. The architecture is best suited to heuristic classification problem-solving ( Clancey, 1985 ), in particular domains with diagnosis or decision-making under uncertainty. Influence diagrams are used as the knowledge structure and computational representation. We present here a set of information and performance requirements for expert-directed knowledge acquisition, and describe a synthesis of approaches for supporting the knowledge engineering activity. We discuss potential applications of INFORM as a knowledge engineering aid, specifically as an aid for developing insight about the encoding domain on the part of its user.

An environment for acquiring semantic information

Abstract: An improved version of IRACQ (for Interpretation Rule ACQuisition) is presented. Our approach to semantic knowledge acquisition: 1) is in the context of a general purpose NL interface rather than one that accesses only databases, 2) employs a knowledge representation formalism with limited inferencing capabilities, 3) assumes a trained person but not an Al expert, and 4) provides a complete environment for not only acquiring semantic knowledge, but also maintaining and editing it in a consistent knowledge base. IRACQ is currently in use at the Naval Ocean Systems Center.

Knowledge-based knowledge elicitation

Abstract: A method for using the advantages of domain-specific knowledge acquisition for a general purpose knowledge acquisition tool is introduced. To adapt the knowledge acquisition tool for a specific application and a specific problem solving strategy (e.g. heuristic classification, such diagnostic strategies as establish and refine), acquisition knowledge bases (AKBs) are integrated in the system to guide the employment of different knowledge elicitation methods (interview techniques, protocol analysis, semantic text analysis and learning mechanisms). Acquisition knowledge bases are predefined deep models, consisting of structured objects to represent important concepts of a domain. These knowledge bases are used in addition to the already acquired knowledge to trigger specific elicitation methods by an analysis of incompleteness and inconsistency of the existing knowledge in the system. Furthermore, methods for integrating these kinds of knowledge acquisition tools with machine learning approaches are discussed.

Compiling design plans from descriptions of artifacts and problem solving heuristics

Abstract: An analysis of the design plans in the Pride expert system shows that they integrate knowledge about structure and functionality of artifacts as well as problem-solving heuristics A method is presented by which such plans can be automatically generated by compiling knowledge about artifacts, problem solving heuristics, and characteristics of specific problems. Knowledge compilation allows the creation of plans tailored to particular problems and offers potential benefits in maintaining a knowledge base, in reusing the same knowledge for different purposes, and in providing a framework for more systematic knowledge acquisition.

Multiple-problem subspaces in the knowledge-design process

Abstract: Designing a knowledge base is viewed as a problem-solving task in which the skilled individual's knowledge and behavior must be mapped into the system, preserving the compiled knowledge acquired by experience. The expert's problem space is complex, but its breakdown into three major subspaces allows one to formalize this approach. Selective interfaces and high-level primitives as well as a flexible knowledge representation not only elicit knowledge and the learning of the design task by the expert. High-level programming, stressing the importance of the psychological as well as the physical descriptions, should allow the expert to bypass the current bottleneck of having to decompile the knowledge into a low-level language and then reconstruct the control structures to recover the expertise. Hence, knowledge design becomes a function available to domain experts themselves. The following reflections aim at the construction of a comprehensive theory of knowledge acquisition and transfer, in the context of a direct relation between the domain expert and the machine. This work is linked to the development and use of the NEXPERT™ hybrid knowledge-based system.

Knowledge base refinement by monitoring abstract control knowledge

Abstract: An explicit representation of the problem solving method of an expert system shell as abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate “endgame” knowledge acquisition. The problem solving method of HERACLES is represented explicitly as domain-independent tasks and metarules. Metarules locate and apply domain knowledge to achieve problem solving subgoals, such as testing, refining, or differentiating between hypothesis; and asking general or clarifying questions. We show how monitoring abstract control knowledge for metarule premise failures provides a means of detecting gaps in the knowledge base. A knowledge base gap will almost always cause a metarule premise failure. We also show how abstract control knowledge plays a crucial role in using underlying domain theories for learning, especially weak domain theories. The construction of abstract control knowledge requires that the different types of knowledge that enter into problem solving be represented in different knowledge relations. This provides a foundation for the integration of underlying domain theories into a learning system, because justification of different types of new knowledge usually requires different ways of using an underlying domain theory. We advocate the construction of a definitional constraint for each knowledge relation that specifies how the relation is defined and justified in terms of underlying domain theories.

Knowledge engineering tools at the architecture level

Abstract: A knowledge system architecture is a level of description of knowledge systems that specialises general AI implementation techniques to suit a class of problem solving tasks. This paper presents three complementary views of the architecture level, and analyzes their implications for the design of knowledge engineering tools. The analysis is illustrated with an architecture for managing uncertainty by reasoning about actions, and with a hierarchy of knowledge engineering tools to support system development and knowledge acquisition at the architecture level.

Outline of a knowledge base model for an intelligent information retrieval system

Abstract: We attempt in this paper to outline a method for the automatic construction of a knowledge base. We propose some methods and a domain knowledge model. A new idea is to conceive a system that is able to each phase of its construction to acquire domain knowledge from all new information that it is building, in particular the indexing terms; the last section is an attempt in this sense.

Eliciting knowledge for software development

Abstract: A current bottleneck in the automation of cognitive tasks, such as software development, is the lack of available, standardized, reliable and valid methods for extracting knowledge from experts. This paper discusses the development of Computer Aided Protocol (CAP) to automatically collect the general and specific cognitive task components of subjects performing a programming task. The effectiveness of CAP is evaluated in a statistically balanced experimental design (n = 30) by comparing it to traditional protocol analysis and a control group. Results indicate that while neither treatment significantly altered the solution process, CAP was able to collect the lower level commands while protocol analysis collected only 56% of these lower level commands. However, protocol analysis was able to obtain significantly more high level goals than CAP. This work suggests that the integration of both protocol and CAP for knowledge extraction would provide more effective information for the development of exp...

A tool for the automatic creation, extension and updating of lexical knowledge bases

Abstract: A tool is described which helps in the creation, extension and updating of lexical knowledge bases (LKBs). Two levels of representation are distinguished: a static storage level and a dynamic knowledge level. The latter is an object-oriented environment containing linguistic and lexicographic knowledge. At the knowledge level, constructors and filters can be defined. Constructors are objects which extend the LKB both horizontally (new information) and vertically (new entries) using the linguistic knowledge. Filters are objects which derive new LKBs from existing ones thereby optionally changing the storage structure. The latter use lexicographic knowledge.

Parallel retrieval and application to conceptual knowledge

Abstract: Humans have the ability to recognize and reason about a wide variety of social behaviors in their fellows: deception, professional relationships, goal seeking, argumentation, etc. Much of this ability is centered around our ability to manipulate conceptual knowledge. There have been a number of computer programs which have demonstrated the capability to use conceptual knowledge as modeled by data structures in symbolic languages. Three fundamental problems are knowledge representation, knowledge access (i.e. which knowledge structures to activate) and knowledge application to specific instances (i.e. which symbols or roles in the knowledge structures are bound to which objects in the current situation). Although results have been enlightening as to processes underlying the use of conceptual knowledge, they have all been based on serial examination of symbolic data structures. This serial approach is unacceptable as a complete model of human performance. This paper presents a distributed connectionist model which retrieves and applies conceptual structures in parallel.

The graphical representation of knowledge as an interface to knowledge based systems

Abstract: This paper reports on a technique for portraying the detail and the extent of knowledge on a graphical interface. Research has centered on concept maps which derive from ideas such as “hypertext”. Such maps represent the structure and inter-connections between concept labels in knowledge structures. A “map” is a bounded view of one aspect of the overall knowledge-data structure. A map is a synonym for a browser. Users can decompress (expand) or compress (reduce to a minimal level) concept relationships based on selection criteria. This map metaphor is based on cognitive theory that supports the representation of knowledge. In knowledge elicitation the chosen interface gives the expert a network metaphor of their knowledge. Each concept (concept label) can be seen as a label with a finite set of links. As the knowledge structure grows a variety of browsers allow the user to see all or part of the knowledge base. Users can select a topic label as the starting point for a browser, define its limits and specify the type of links (relationships) to be included. Such an interface is a powerful tool as it uses the human eye, which has under used channel capacity, to process complex data structures. The system has been implemented on a workstation which allows multiple windows in aWIMP environment. Knowledge can be entered at any level, compressed (top to down) or decompressed (bottom to up). The system (called KIM: Knowledge and Information Mapping ) keeps integrity between different views. The paper reports on user trials with the system. It suggests that problems can arise when the user can not easily reconcile different views and perspectives. The paper highlights the importance of “terrain knowledge” of knowledge based systems (global views), in addition to “street knowledge” (local views).

The boundary between word knowledge and world knowledge

Abstract: I will focus my comments on linguistic considerations concerning the interrelat ion between words and world representat ion and will argue that word knowledge must be kept distinct from world knowledge. Knowing everything about a referent object or event with which a word is associated is not enough to allow one to use a word appropriately. World knowledge must be supplemented and constrained by linguistic knowledge to yield an appropriate account of word knowledge.