Database concepts for the support of knowledge-based systems

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. >

Proposes a new model, called the Drama model, under the object-oriented paradigm, for representing the rule base of general expert systems and rule-based applications. Based on the Drama model, modules of rules can be constructed as a reusable, sharable and modifiable rule package, which can then be shared in heterogeneous applications. To manage rule packages effectively and efficiently, an object-oriented rule-base management system (OORBMS), which supports independent procedures of rule editing and rule execution for various kinds of applications, is also developed. Comparing the OORBMS to traditional rule-base management systems (RBMSs) or knowledge-base management systems (KBMSs) from the viewpoint of rule management, it provides higher rule-base quality in five aspects: modifiability, reusability, modularity, flexibility and extendibility.

Knowledge-based management systems (KMS) are valuable tools that enable executives to formulate and execute strategic decisions in their organizations. However, implementation of a KMS is complex and laden with several potential risks. Using a framework of the socio-technical model, we conduct a case study to track risks in a KMS. Guided by the depth of task-technology task theory and the principles of consonance, we present expectations and early results regarding the risks that derive from mismatches among the various dimensions. In order to asses and better understand the risk associated with implementing a KMS, interviews were conducted with the employees of the NTA (National Tax Administration), FDC (Financial Data Center), and CHT (Chunghwa Telecom). The interviews centered on the detailed experiences of the participants implementing and working with KMS projects at their respective companies. Analysis of the interviews considered the socio-technical model and confirmed an ability to designate risks to both the project and the product of development through dimensions of task, actor, structure and technology as well as the fit between each combination.

KNOMAD, a knowledge management design system, is a collection of tools built in layers. The layered architecture provides two major benefits: the ability to flexibly apply only those tools that are necessary for an application, and the ability to deep overhead, and thus inefficiency, to a minimum. KNOMAD is designed to manage many knowledge bases in a distributed environment. Three very different knowledge agents have been defined so far. The Freight Agency Knowledge Base is the simplest. However, it was made more complex by using a meta-rule group for its control strategy. The 'monkeys and bananas' problem has been defined; it consists of about twenty rules, uses qualification extensively and has been very useful to drive out limitations and inadequacies of the rule system. Finally, a large knowledge base consisting of three rule groups and a total of about 160 rules has been defined that performs fault diagnosis in a power management and distribution system for a space-station-like platform.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The paper proposes a knowledge-based virtual asset management system to help manufacturers with virtual production operations management in a dispersed manufacturing network. The architecture of the proposed system is built using rule-based reasoning, Radio Frequency Identification (RFID) and production cost minimisation model. The customised production cost minimisation model helps manufacturers select suitable subcontracted manufacturers during the reconfiguration of dispersed manufacturing network. Furthermore, through adopting the knowledge-based concept and RFID technology, the proposed system is capable of discovering potential asset problems among the dispersed production entities. The system architecture and managerial implication are discussed.

A useful strategy for integrating AI and DB technology is coupling PROLOG with a relational database management system (RDBMS). One of the main difficulties of this approach is the evaluation of recursively defined queries. We will therefore give a short overview of the different possibilities for such strategies. Then we describe the RQA/FQI Strategy which is complete over the domain of function-free Horn clauses and uses a two step architecture for evaluating a recursive query. We further compare our strategy to other query processing strategies and also discuss related concepts from the area of theorem proving.

The OSAM*.KBMS is a knowledge-base management system, or the so-called next-generation database management system, for non-traditional data/knowledge-intensive applications. In order to define, query, and manipulate a knowledge base, as well as to write codes to implement any application system, we have developed an object-oriented knowledge-base programming language called K to serve as the high-level interface of OSAM*.KBMS. This paper presents the design of K, its implementation, and its supporting KBMS developed at the Database Systems Research and Development Center of the University of Florida.

Semantic expressive representation of design objects, active system behavior combined with reasoning facilities, and efficient implementation concepts are necessary requirements for the construction of better CAD systems. Here, we describe our approach to a knowledge base management system and exemplify its usage for advanced CAD systems.

The nature of a hypermedia-based knowledge-based management system (KBMS) admitting default knowledge is examined. The associative reasoning of hypermedia has been utilized to provide both probabilistic and logical interpretations to default rules. The knowledge acquisition process for one such system is challenging, because it must be done under a controlled experiment permitting Bayesian interpretation of default facts and composition of hard rules in logical form. This obviously entails a very large knowledge base. Both the procedural (logic) and empirical (probabilistic) configuration of default situations on the hypermedia help in focusing only a limited subset of the whole knowledge base. However, the success of the KBMS designer will depend on the power of the abstraction tools provided. >

The Knowledge Base Management Systems (KBMS) Project at the University of Toronto (1985-1995) was inspired by a need for advanced knowledge representation applications that require knowledge bases containing hundreds of thousands or even millions of knowledge units. The knowledge representation language Telos provided a framework for the project. The key results included conceptual modeling innovations in the use of semantic abstractions, representations of time and space, and implementation techniques for storage management, query processing, rule management, and concurrency control. In this paper, we review the key ideas introduced in the KBMS project, and connect them to some of the work since the conclusion of the project that is either closely related to or directly inspired by it.

In this paper, a knowledge-based information system for the plant operation of steel making company has been proposed. The system, which is named as K-VRS(Knowledge-based Virtual Reality System), provides a connection between ERP plant maintenance module and knowledge-based engineering methodologies, and thus, enables network-based highly effective plant maintenance process. The developed system is expected to play a great role for more efficient and safer plant maintenance.

A knowledge-based software project management system called Dr. Watson is being developed using the automated reasoning tool (ART) and Common Lisp on Symbolics workstations. Dr. Watson has evolved over 1-1/2 years from a simple prototype of two to three pages of ART code to a system composed of 26 files; the current system comprises a conceptual model of project management, algorithms, and reasoning processes for project planning and scheduling, consistency and completeness checking, knowledge-aggregation capabilities, and a direct-manipulation graphics interface. Dr. Watson's evolution has been smooth and controlled because of the techniques used to structure both the prototype and the prototyping process. The authors discuss these techniques, the process model used to organize Dr. Watson's evolution, and the lessons learned from the project. >

There is an increasing need to apply emerging technologies in knowledge-based management system. This proposed system captures the knowledge of experts and the knowledge acquired for designing a new systems. Wind Tunnel Test data of missiles has been taken into consideration for knowledge management. Interpolation and Extrapolation of missile test data has been attempted using neural network technique General Regression Neural Networks training algorithm. The results produced by neural network training methodologies are validated with the existing test data. The knowledge extraction using neural network is found suitable for interpolation and to some extent for extrapolation, thereby reducing the cost as well as number of test runs in Wind Tunnel Test.

Les donnees statistiques habituellement recueillies sur les etudiants et leur cursus pourraient etre exploitees en vue de rationaliser les procedures de selection et de recrutement. Expert 4 est un systeme de gestion de bases des connaissances concu pour la description precise des connaissances dans le domaine bio-medical qui permet a l'utilisateur de resumer et de traiter les donnees entrees dans le systeme. Il necessite un IBM PC avec une memoire vive de 128 K. L'auteur decrit une ebauche d'application dans le domaine pre-cite exploitant les donnees sur deux groupes d'etudiants. (INTD)

Numerous manufacturing and service organizations have integrated or are considering integration of their respective occupational health and safety management and audit systems into the International Organization for Standardization-based (ISO) audit-driven Quality Management Systems (ISO 9000) or Environmental Management Systems (ISO 14000) models. Companies considering one of these options will likely need to identify and evaluate several key factors before embarking on such efforts. The purpose of this article is to identify and address the key factors through a case study approach. Qualitative and quantitative comparisons of the key features of the American Industrial Hygiene Association ISO-9001 harmonized Occupational Health and Safety Management System with The Goodyear Tire & Rubber Co. management and audit system were conducted. The comparisons showed that the two management systems and their respective audit protocols, although structured differently, were not substantially statistically dissimilar in content. The authors recommend that future studies continue to evaluate the advantages and disadvantages of various audit protocols. Ideally, these studies would identify those audit outcome measures that can be reliably correlated with health and safety performance.