Showing papers on "Knowledge extraction published in 1990"

Interactive, cross-referenced knowledge system

Abstract: A knowledge system having a development configuration by which a knowledge engineer enters knowledge content into a database, and a user configuration employed by the end user to access the database for interactive learning, information retrieval, and problem solving in a specified subject area. The knowledge is organized by a hierarchy of topic nodes, with each node having an associated plurality of cross referenceable information units representing a variety of types, or categories, of information. The user can control the navigation path and information display sequence among information units in accordance with personal learning needs and style. One category can include a pattern of prompts and possible responses. The separation of knowledge content from program logic permits non-programmers to set up, modify, and maintain the knowledge content of the system.

Knowledge and natural language processing

Abstract: KBNL is a knowledge-based natural language processing system that is novel in several ways, including the clean separation it enforces between linguistic knowledge and world knowledge, and its use of knowledge to aid in lexical acquisition. Applications of KBNL include intelligent interfaces, text retrieval, and machine translation.

Visual knowledge engineering

Abstract: The knowledge engineer is only weakly supported at three critical stages in the knowledge engineering life cycle: (1) knowledge acquisition during which problem conceptualization must largely be tackled with paper and pencil; (2) knowledge encoding, during which it is frequently necessary to be able to navigate across a variety of knowledge representation formalisms; and (3) large-scale debugging, in which the graphical rule traces cannot cope with enormous rule sets involving hundreds or thousands of rules. The research described attempts to provide just such support through complementary visual programming (VP) and program visualization (PV) techniques embedded in a fully implemented software environment called KEATS: the knowledge engineer's assistant. Several novel visual programming and program visualization techniques aimed at knowledge engineers have been developed, which include (1) a hypertext transcript analyzer from which conceptual models can be generated, (2) a direct graph manipulation sketchpad which allows the knowledge engineer to sketch out objects and relations (including control flow and rule dependencies) from which code can be generated, and (3) dependency viewers which allow the knowledge engineer to examine and manipulate temporal and logical rule dependencies at different levels of granularity. How these facilities are incorporated into KEATS and the key themes that emerge from this approach to visual knowledge engineering are discussed. >

A G-net model for knowledge representation and reasoning

Abstract: The G-net model for G-type knowledge representation is introduced. It is capable of modeling both static semantic knowledge and dynamic control knowledge, combining them into a loosely coupled, mixed-type knowledge hierarchy. Four reasoning algorithms for the G-net model are proposed: inheritance reasoning and recognition reasoning for semantic knowledge, event-driven reasoning for dynamic knowledge, and control table reasoning for coordination and control in a mixed-type knowledge hierarchy. Based on the knowledge-table representation, the G-net model expresses the constraints and relationships among knowledge objects explicitly so that reasoning algorithms can be implemented efficiently. Applications to information systems prototyping are discussed. >

Querying database knowledge

Abstract: The role of database knowledge is usually limited to the evaluation of data queries. In this paper we argue that when this knowledge is of substantial volume and complexity, there is genuine need to query this repository of information. Moreover, since users of the database may not be able to distinguish between information that is data and information that is knowledge, access to knowledge and data should be provided with a single, coherent instrument. We provide an informal review of various kinds of knowledge queries, with possible syntax and semantics. We then formalize a framework of knowledge-rich databases, and a simple query language consisting of a pair of retrieve and describe statements. The retrieve statement is for querying the data (it corresponds to the basic retrieval statement of various knowledge-rich database systems). The describe statement is for querying the knowledge. Essentially, it inquires about the meaning of a concept under specified circumstances. We provide algorithms for evaluating sound and finite knowledge answers to describe queries, and we demonstrate them with examples.

An expert-system shell using structured knowledge: an object-oriented approach

Abstract: An architecture for an expert-system shell that mixes declarative nd procedural knowledge, overcoming a major problem of conventional shells, is presented. The prototype shell uses structured knowledge representations and its built-in database interface not only allows automatic extraction of data from a database management system but also provides a fuzzy database query facility. The shell's object-oriented approach to knowledge representation supports data and knowledge acquisition and management. Another feature is encapsulation which prevents object manipulation except by defined operations. A comparison of representation methods and two case studies showing System X-I's power and flexibility are included. >

Developing a tool for knowledge integration: initial results

Abstract: Knowledge integration is the task of incorporating new information into existing knowledge. The task is difficult because the consequences of an addition to an extensive knowledge base can be numerous and subtle. Current methods for automated knowledge acquisition largely ignore this task, although it is increasingly important with the move toward large scale, multifunctional knowledge bases. To study knowledge integration, we propose to develop and evaluate a knowledge-acquisition tool that helps with extending a knowledge base through interaction with a knowledge engineer. An initial prototype of this tool has been implemented and demonstrated on a complex extension to a large knowledge base.

Structuring knowledge bases using automatic learning

Abstract: The need to add an automatic learning phase to the construction process of a knowledge base is stressed. This work introduces a new technique based on machine learning methodologies which automatically creates a particular knowledge representation structure called knowledge space, from which common generalizations of knowledge objects may be efficiently inferred. Also introduced are some of the improved and added processing capabilities made possible by this structure. >

Using a group decision support system environment for knowledge acquisition: a field study

Abstract: A field study using a GDSS (group decision support systems) environment to acquire knowledge from multiple experts to build an expert system for an information center was conducted. This field study indicated that a GDSS environment facilitates the acquisition of knowledge from a group of experts by documenting knowledge electronically, by supporting knowledge extraction from individual experts in a parallel fashion, by offering possibilities to resolve conflicts during the knowledge extraction phase, and by providing a group interaction atmosphere to enrich the domain of expertise. In addition, it was found that structured analysis techniques were useful in planning for knowledge acquisition and that a designated primary expert was of great help when dealing with multiple experts. >

Knowledge management for expert systems

Abstract: As more and more knowledge based applications (such as expert systems) are developed, the problem of duplication of knowledge will become apparent. The solution to this problem lies in the concept of the knowledge base management system (KBMS), which is a repository for knowledge in the same way that a DBMS is a repository for data. Such a system would store the knowledge required by different knowledge based applications in a single base. One important property which is essential for a KBMS is that it should provide knowledge in the form that the knowledge base application requires it. This makes the design of a KBMS considerably complicated. The authors describe a system developed as a first step towards a full KBMS. The system is based on a logic database management system (LDBMS) or deductive database management system (DDBMS) with appropriate extensions.

Modular blackboard based expert system

Abstract: An expert system includes a blackboard, a plurality of knowledge sources, a control knowledge source and a control module. The blackboard stores data used during an execution cycle. Each knowledge source includes rules for performing selected operations in connection with the data in the blackboard. The control knowledge source includes selection rules for selecting among the knowledge sources. The control module performing an execution cycle including an eligibility determination phase to identify one or more of the knowledge sources, a knowledge source selection phase using the selection rules in said control knowledge source to select one of the identified knowledge sources, and an action phase to process a rule of the selected knowledge source.

A Comparison of the Process of Knowledge Elicitation with that of Information Requirements Determination

Abstract: It is argued that the process of knowledge elicitation differs substantially from that of traditional systemsanalysis. These differences are identified and described. The implication of this observation is thatsignificant retraining of information systems professionals and reorientation of management will berequired if knowledge based systems are to be used extensively in business organizations.

Applying systems analysis techniques to knowledge engineering

Abstract: This paper deals with the issue of knowledge elicitation for expert systems. Specifically, it looks at the requirements of the knowledge elicitation process and the suitability of structured methods from systems analysis to carry out part of the elicitation task. The techniques of data flow analysis, entity-relationship analysis and entity-life cycle analysis are used to structure the data associated with the expert task. The methods proposed lay emphasis on the definition of limited data sets at the boundary of the explicit knowledge base and the identification of status attributes to model the control of activation of ‘processes’ within the knowledge base. Attention is also paid to the relationship between the resulting logical model, and two popular methods of knowledge representation, namely, Production Systems and Frames.

A knowledge engineered linear programming formulation assistant

Abstract: A study is presented which involves eliciting methods used by OR (operations research) experts in building LP (linear programming) formulations for textbook problem statements. As a first step, the authors pay special attention to eliciting the knowledge representation (KR) schema used by OR experts, an aspect which previous work in this area left to the knowledge engineer's discretion. The authors argue that a more 'natural' system can be developed by using expert KR schema. >

Knowledge bases and databases: converging technologies, diverging interests

Abstract: The convergence of knowledge-based and database technologies in the development of expert databases which store not only values but chunks of knowledge is discussed. The knowledge-based programming perspective is examined, and differences between knowledge bases and databases are identified. It is argued that in spite of converging technologies, the interests of knowledge-base specialists and database specialists differ, and their research directions diverge. >

Designing deontic knowledge bases from regulation texts

Abstract: In any organization, large amounts of knowledge are stored in various textual forms — informative texts, norms, procedures, regulations, etc. This research explores the possibility of creating knowledge bases by exploiting information contained in such texts. The documents analysed correspond to 'prescriptive texts' which can be found in companies, such as norm books, regulations, instruction books, etc. The current work concentrates on French versions of regulation texts from the Government of Quebec, and presents details of a knowledge acquisition system that can analyse prescriptive texts using a text-structuring approach which distinguishes the macrostructure, the microstructure and the domanial component of a text. The main characteristics of a regulation text microstructure are described, and a model to represent deontic knowledge in a knowledge base is proposed. The general architectures of a knowledge acquisition system and of a consultation system which enables the manipulation of deontic knowledge bases are presented.

An approach to DBS-based knowledge management

Abstract: This paper discusses the requirements of knowledge management from the point of view of Database Systems. Primarily, it focuses on the practical investigations that led to a DBS-based architecture for an effective and efficient management of large knowledge bases. This architecture, called KRISYS, is described, thereby concentrating on the mechanisms used to support the nearby application locality concept as well as to map knowledge structures onto secondary storage.

Techniques for automatic test knowledge extraction from compiled circuits

Abstract: In the past, research has shown that the use of high-level test knowledge can be used to greatly accelerate the test generation process. The problem was that no techniques were developed to extract this knowledge from a circuit. Typically, the only solution for a circuit designer was to manually extract the test knowledge. When designers are using sophisticated high-level synthesis tools (e.g., a silicon compiler), the designer may not be competent to extract this type of knowledge. In this thesis, solutions to the problem of automatically extracting this high-level knowledge from the structure of a compiled circuit are presented. Two different types of knowledge are addressed. The first type of knowledge is a testability measure. We present solutions to the problem estimating the testability for circuits defined at a functional level. By using an information theoretic testability measure, the concepts of controllability and observability are captured. Instead of requiring exhaustive enumeration of the input space to compute the measure (as has been previously suggested), we presented two different methods for efficiently and accurately estimating the measure. In addition, we have present various applications of the measure, including automatic circuit partitioning and test point insertion. The second type of knowledge is used in test generation. We describe techniques to automatically extract high-level test and DFT knowledge from the structure of compiled circuits. These techniques work autonomously and require no user intervention. This system has been implemented in a SUN workstation environment and is known as DELPHI. It operates on the high-level dataflow representation of a compiled circuit and generates the test knowledge in the form of lists of primary input assignments. Achieving both high levels of fault coverage and fast performance, DELPHI can extract test knowledge from both non-sequential and sequential circuits. When test knowledge extraction is unsuccessful, additional DFT knowledge is obtained to efficiently represent design for testability options. In those cases in which users are able to provide test knowledge, techniques to verify user-provided knowledge are described.

Modular construction of logic knowledge bases: an algebraic approach

Abstract: Modularity, reusability, extension capabilities and transformations are among the main facilities that must be considered in knowledge engineering targeted to the development of knowledge bases in the large. Moreover, fast and efficient techniques for updating, querying and inferencing with knowledge bases are also important issues. It seems that current knowledge representation approaches, namely the logic and the structural approaches, do not cope with the essential aspects that must characterize a knowledge engineer workbench. Herein, an algebraic approach allowing the modular construction of knowledge bases is proposed. The main building blocks of knowledge bases are the semantic primitives defined by theory morphisms, mappings taking knowledge bases into knowledge bases, and an interpretation functor. The application of a semantic primitive is the value of the interpretation functor for the chosen interpretations of the argument knowledge bases of the theory morphisms involved. A knowledge base becomes a structured theory from which we can retrace the respective construction sequence. Incidentally, the approach allows the description of a new architecture for knowledge engineering supporting two alternative representations of knowledge bases: a data base representation and a theory representation. Fast, concurrent and dumb updating and querying of knowledge bases can be targeted to the data base representation, whereas intelligent accesses can be directed to the theory representation. The structure of the knowledge base makes possible the selection of the relevant fragment for the purposes at hand. The E-R modeling concepts are adopted for illustrating the approach. The structuring aspects are discussed upon an E-R example of a fragment of a stock management knowledge base.

Modeling operations management support systems

Abstract: An abstract model for operations management support systems (OMSSs) is proposed. These are systems that provide end-users with intelligent support based on comprehensive and up-to-date knowledge of overall system status. In the model, a system is represented as a collection of distributed intelligent objects, each encapsulating data, process, and other knowledge associated with an operation. The model uses production rules and separates data, procedural knowledge, and control knowledge within an object. It is demonstrated how to extend object modeling to include control knowledge within objects and how to introduce structure and software engineering principles in knowledge modeling systems such as production systems. >

Efficient knowledge representation systems

Abstract: Frame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge representation including user-defined inheritance and relations, object-oriented programming, and constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.

Knowledge acquisition as knowledge assimilation

Abstract: KNOWLEDGE BASES ARE CONSTRUCTED AND REFINED USING INFORMATION OBTAINED FROM DOMAIN EXPERTS. THE ASSIMILATION OF THIS INFORMATION INTO AN EXISTING KNOWLEDGE BASE IS AN IMPORTANT FACET OF THE KNOWLEDGE ACQUISITION PROCESS. THIS KNOWLEDGE ASSIMILATION REQUIRES AN UNDERSTANDING OF HOW THE NEW INFOR- MATION CORRESPONDS TO THAT ALREADY KNOWN BY THE SYSTEM AND HOW THIS EXIST- ING INFORMATION MUST BE MODIFIED SO AS TO REFLECT THE EXPERT''S VIEW OF THE DOMAIN. THIS PAPER DESCRIBES A SYSTEM, K(SUPERSCRIPT)N(SUBSCRIPT)AC, THAT MODI- FIES AN EXISTING KNOWLEDGE BASE BASED ON A DISCOURSE WITH A DOMAIN EXPERT. USING HEURISTIC KNOWLEDGE ABOUT THE KNOWLEDGE ACQUISITION PROCESS, KNAC (AS ABOVE) ANTICIPATES MODIFICATIONS TO THE EXISTING ENTITY DESCRIPTIONS. THESE ANTICIPATED MODIFICATIONS, OR `EXPECTATIONS'', ARE USED TO PROVIDE A CONTEXT IN WHICH TO ASSIMILATE THE NEW DOMAIN INFORMATION.