MRM: A matrix representation and mapping approach for knowledge acquisition

Knowledge acquisition plays a critical role in constructing a knowledge-based system (KBS). It is the most time-consuming phase and has been recognized as the bottleneck of KBS development. This paper presents a matrix representation and mapping (MRM) approach to facilitate the effectiveness of knowledge acquisition in building a KBS. The proposed MRM approach, which is based on matrix representation and mapping operations, comprises six consecutive steps for generating rules. The procedure in each step is elaborated. A case study on primarily diagnosing an automotive system is employed to illustrate how the MRM approach works.     

A fuzzy genetic algorithm for the discovery of process parameter settings using knowledge representation

In this paper, we propose a fuzzy genetic algorithm (Fuzzy-GA) approach integrating fuzzy rule sets and their membership function sets, in a chromosome. The proposed approach consists of two processes: knowledge representation and knowledge assimilation. The knowledge of process parameter setting is encoded as a string with a fuzzy rule set and the associated membership functions. The historical process data forming a combined string is used as the initial knowledge population, which is then ready for knowledge assimilation. A genetic algorithm is used to generate an optimal or nearly optimal fuzzy set and membership functions for the process parameters. The originality of this research is that the proposed system is equipped with the ability to take advantage of assessing the loss which is caused by discrepancy with a process target, thereby enabling the identification of the best set of process parameters. The approach is demonstrated by the use of an experimental example drawn from a semiconductor manufacturer and the results show us that the suggested approach is able to achieve an optimal solution for a process parameter setting problem.     

A knowledge engineering approach to knowledge management

Knowledge management facilitates the capture, storage, and dissemination of knowledge using information technology. Methods for managing knowledge have become an important issue in the past few decades, and the KM community has developed a wide range of technologies and applications for both academic research and practical applications. In this paper, we propose a knowledge engineering approach (KMKE) to knowledge management. First, a knowledge modeling approach is used to organize and express various types of knowledge in a unified knowledge representation. Second, a verification mechanism is used to verify knowledge models based on the formal semantics of the knowledge representation. Third, knowledge models are classified and stored in a hierarchical ontology system. Fourth, a knowledge query language is designed to enhance the dissemination of knowledge. Finally, a knowledge update process is applied to modify the knowledge storage with respect to users’ needs. A knowledge management system for computer repair is used as an illustrative example.     

High Performance Knowledge Bases: four approaches to knowledge acquisition, representation and reasoning for workaround planning

As part of the DARPA-sponsored High Performance Knowledge Bases program, four organisations were set the challenge of solving a selection of knowledge-based planning problems in a particular domain, and then modifying their systems quickly to solve further problems in the same domain. The aim of the exercise was to test the claim that, with the latest AI technology, large knowledge bases can be built quickly and efficiently. The domain chosen was ‘workarounds’; that is, planning how a convoy of military vehicles can ‘work around’ (i.e. circumvent or overcome) obstacles in their path, such as blown bridges or minefields.

This paper describes the four approaches that were applied to solve this problem. These approaches differed in their approach to knowledge acquisition, in their ontology, and in their reasoning. All four approaches are described and compared against each other. The paper concludes by reporting the results of an evaluation that was carried out by the HPKB program to determine the capability of each of these approaches.     

A frame knowledge system for managing financial decision knowledge

Managing decision knowledge or expertise from domain experts is one of the most exciting challenges in today’s knowledge management field. The nature of decision knowledge in determining a firm’s financial health is context-dependent, intangible, and tacit in nature. Knowledge-based systems (KBS) have been recognized as a successful paradigm for managing financial decision knowledge attributed to possessing capabilities of reasoning and enhancing the consistency of decision-making. However, most present KBS adopt rules as the knowledge representation scheme, which cannot express the expert’s knowledge construct systematically when dealing with more numerous and disordered knowledge connotations. In addition, the standalone nature of the systems hinders them from deploying onto heterogeneous platforms and cannot accommodate to the emerging Web-enabled environment. To reduce these flaws, this study proposes a frame knowledge system in which the structural and procedural decision knowledge is encapsulated so that unnecessary interference can be avoided. A protocol analysis, before encapsulation, is conducted to elicit the tacit and unstructured knowledge from a senior CPA we cooperated with. The deployment and Web enabling issue is tackled by using Jess and Java interoperable computing. With these combined, it is possible to prompt the understandability, accessibility, and reusability of KBS. The effectiveness of the proposed system is validated in supporting the expert’s decision-making by conducting an empirical experimentation on 537 companies listed in the Taiwan Stock Exchange Corporation.     

A knowledge extraction and representation system for narrative analysis in the construction industry

Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.     

Acquiring,storing and utilizing process planning knowledge using neural networks

An approach is formulated for the automated acquisition of process selection and within-feature process sequencing knowledge from examples using neural networks. Network architecture, problem representation and performance issues are discussed.     

The evolutionary perspective of knowledge creation – A mathematical representation

Since knowledge and its strategic applications have now become a major source of competitive advantage for organizations, the lifeblood of organizations is to possess the capabilities for acquiring, creating, sharing, diffusing, utilizing, and storing knowledge among organizational members. Among the capabilities, knowledge creation is the major resource of organizational innovation and it, therefore, plays a more crucial role in developing a sustained competitive advantage for organizations, especially in a dynamic environment. Although numerous papers have studied the issue of knowledge creation, those papers focused on conceptualizing the process of knowledge creation and did not investigate the concrete problem of knowledge creation. In the current paper, a mathematical representation of knowledge creation based on an evolutionary perspective is used to understand: (1) if knowledge can be created; (2) how knowledge is created; (3) under what conditions knowledge can be created; (4) the partial effects of variables which affect the process of knowledge creation.     

A decidable first-order logic for knowledge representation

Decidable first-order logics with reasonable model-theoretic semantics have several benefits for knowledge representation. These logics have the expressive power of standard first order logic along with an inference algorithm that will always terminate, both important considerations for knowledge representation. Knowledge representation systems that include a faithful implementation of one of these logics can also use its model-theoretic semantics to provide meanings for the data they store. One such logic, a variant of a simple type of first-order relevance logic, is developed and its properties described. This logic, although extremely weak, does capture a non-trivial and well-motivated set of inferences that can be entrusted to a knowledge representation system.This is a revised and much extended version of a paper of the same name that appears in the Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, California, 1985.     

Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour

This paper describes an on-line fault diagnosis system which diagnoses faults in a pilot scale mixing process using on-line measurements. Fault detection and fault diagnosis is performed based on a qualitative model of the mixing process. The qualitative model provides a set of constraints for the system being diagnosed. Once it is violated, a particular fault is detected. Since most of the information used by the diagnosis system comes from on-line measurements, it is important to determine whether sensors are working normally or not before considering failures of other components. Sensor failure is mainly diagnosed from heuristic considerations, while the failures of other components are diagnosed from a procedure of hypothesis generation, qualitative simulation, and comparison. Based on a hypothesis, the behaviour of the system being diagnosed is simulated from its qualitative model and is compared with the actual measurements. Depending upon whether they conflict or not, the hypothesis is denied or retained. A new approach for reducing the ambiguity in qualitative simulation is described. Ambiguity is reduced by taking account of the information on the order of magnitude relations between different physical variables.     

Knowledge acquisition and knowledge representation with class: the object-oriented paradigm

Knowledge acquisition and knowledge representation are the fundamental building blocks of knowledge-based systems (KBSs). How to efficiently elicit knowledge from experts and transform this elicited knowledge into a machine usable format is a significant and time consuming problem for KBS developers. Object-orientation provides several solutions to persistent knowledge acquisition and knowledge representation problems including transportability, knowledge reuse, and knowledge growth. An automated graphical knowledge acquisition tool is presented, based upon object-oriented principles. The object-oriented graphical interface provides a modeling platform that is easily understood by experts and knowledge engineers. The object-oriented base for the automated KA tool provides a representation independent methodology that can easily be mapped into any other object-oriented expert system or other object-oriented intelligent tools.     

Restructuring decision tables for elucidation of knowledge

Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.     

A Delphi study of knowledge management systems: Scope and requirements

We empirically explored the roles and scope of knowledge management systems in organizations. Building on a knowledge-based view of the firm, we hypothesized and empirically tested our belief that more integration is needed between technologies intended to support knowledge and those supporting business operations. Findings from a Delphi study and in-depth interviews illustrated this and led us to suggest a revised approach to developing organizational knowledge management systems.     

Building ontology based knowledge maps to assist business process re-engineering

Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

Evaluation of knowledge management tools using AHP

This paper presents an application of the analytic hierarchy process (AHP) used to select the most appropriate tool to support knowledge management (KM). This method adopts a multi-criteria approach that can be used to analyse and compare KM tools in the software market. The method is based on pairwise comparisons between several factors that affect the selection of the most appropriate KM tool. An AHP model is formulated and applied to a real case of assisting decision-makers in a leading communications company in Hong Kong to evaluate a suitable KM tool. We believe that the application shown can be of use to managers and that, because of its ease of implementation, others can benefit from this approach.     

Frame-based representation of philosophical systems using a knowledge engineering tool

This article addresses the methodological problem of the non-linear representation of philosophical systems in a computerized knowledge base. It is a problem of knowledge representation as defined in the field of artificial intelligence. Instead of a purely theoretical discussion of the issue, we present selected results of a practical experiment which has in itself some theoretical significance. We show how one can represent different philosophies using CODE, a knowledge engineering system developed by artificial intelligence researchers. The hypothesis is that such a computer based representation of philosophical systems can give insight into their conceptual structure. We argue that computer aided text analysis can apply knowledge representation tools and techniques developed in artificial intelligence and we estimate how philosophers as well as knowledge engineers could gain from this cross-fertilization. This paper should be considered as an experiment report on the use of knowledge representation techniques in computer aided text analysis. It is part of a much broader project on the representation of conceptual structures in an expert system. However, we intentionally avoided technical issues related to either Computer Science or History of Philosophy to focus on the benefit to enhance traditional humanistic studies with tools and methods developed in AI on the one hand and the need to develop more appropriate tools on the other. Gilbert Boss is professor of Philosophy at Université Laval, Québec. He is the author of several books, including Les machines à penser. L'homme et l'ordinateur,Zurich: Grand Midi, 1987, and John Stuart Mill. Induction et utilité,Paris: PUF, 1990. His main fields of research are modern philosophy, philosophy of culture, philosophical discourse and systems, artificial intelligence. Maryvonne Longeart is professor of Computer Science at UQAH, Hull, Québec. Her research interests include object oriented design methodologies and knowledge representation. She received a PhD in Philosophy from the University of Ottawa in 1978 and a BSc in computer science in 1987. She contributed to the Encyclopédie philosophique universelle,PUF, 1992 and published several papers on the representation of complex conceptual systems.Douglas Skuce is professor of computer science at the University of Ottawa. He has worked in the area of knowledge engineering since his PhD (McGill, 1977). During 1978-present he has been developing the CODE system for various applications, including terminology and software development. Currently, his interests include designing ontologies for knowledge exchange and coupling large corpora to systems such as CODE.     

A PDES/STEP-based information model for computer-aided process planning

Computer Aided Process Planning (CAPP) constitutes one of the most essential elements in Computer Integrated Manufacturing (CIM). Although many CAPP systems have been reported in literature during the last two decades, few of them are compatible enough to integrate easily with other systems in the CIM environment. One major reason is the lack of an effective method to represent the information required by CAPP, and to unify such information with the information of other systems in the CIM environment. Indeed, this problem has received relatively inadequate attention in the recent research of CAPP systems. In this paper, an information model for CAPP is developed by using the object-oriented modeling and the Product Data Exchange Step/STandard of Exchange Product data (PDES/STEP) techniques. The model consists of the part information model, the process plan information model, and the production resource information model. The EXPRESS language or the EXPRESS-G diagram is used to represent these models. Indeed, the proposed information model will greatly improve the CAPP system's capability of effective integration with other systems in the CIM environment, and, ultimately, to facilitate the implementation of the whole CIM strategy in manufacturing enterprises.