A Frame-based Object Relational Expert Database System

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

The purpose of this paper is to describe the process of OWL ontology merging and member function assignment for ontology elements. Ontology merging and member function assignment are necessary for constructing a Semantic Web Expert System (SWES) knowledge base. We use the acronym SWES to refer to expert systems which are capable of processing OWL ontologies from the Web with the purpose to supplement or even to develop their knowledge base. To our knowledge, the tasks of ontology merging and member function assignment for SWES have not yet been investigated.KeywordsOntology MergingSemantic WebExpert Systems

In present study, a hierarchical knowledge-based expert system, named HYDROPERT, to predict binary azeotrope formation in hydrocarbon mixtures was created and investigated. Specific knowledge included in azeotropy on hydrocarbon mixtures and the implementation of the expert system are described along some of the several components of expert system applications: knowledge representation strategy and levels of knowledge abstraction, inference machine, user interface, explanation facilities. The knowledge base is hierarchically structured with the multiple levels of domain-specific knowledge such as the azeotropic data bank as the lowest level, component-specific compiled heuristic rules as the second level, group-oriented compiled heuristic rules as the third level, and generic class-oriented model-based heuristic rules as the highest level. The predictive capabilities and generality of the expert system can be highly enhanced through the integration of different kinds of domain knowledge into the hierarchical structure. The expert system predicting the binary azeotrope formation in hydrocarbon mixtures may be a useful tool for many chemical engineering activities, especially such as process synthesis and design.

Ebinformatics: Ebola fuzzy informatics systems on the diagnosis, prediction and recommendation of appropriate treatments for Ebola virus disease (EVD)

Expert system knowledge represents expertise obtained through formal education, training, and/or experience. Formal education provides deep knowledge of a particular domain; experience and training result in heuristic knowledge. A knowledge base defines the range of information and understanding with which the system is capable of dealing; therefore, its information must be structured and filed for ready access. The objective of this symposium is to address the challenges associated with establishment of valid expert system knowledge, specifically, knowledge to be used by expert system shells. As expert system knowledge is obtained, structured, and stored, it is formulated. In this symposium, knowledge formulation is addressed as a three-phase process: knowledge acquisition, the mechanics associated with structuring knowledge, and knowledge porting. Knowledge acquisition is the process of extracting expertise from a domain expert. Expertise may be collected through a series of interviews between the expert and a knowledge engineer or through sessions the expert holds with an automated knowledge acquisition tool. Thus, the ultimate outcome of knowledge acquisition is a collection of raw knowledge data. The following human factors issues become apparent: documenting mental models (where mental models are the expert's conceptualization of a problem), recording cognitive problem-solving strategies, and specifying an appropriate interface between the domain expert and the acquisition methodology. The knowledge structuring process involves the refinement of raw knowledge data, where knowledge is organized and assigned a semantic structure. One issue that must be considered is how to interpret knowledge data such that formal definitions, logical relationships, and facts can be established. Finally, formulation involves knowledge porting, that is, the movement of an expert system shell's knowledge base to various other shells. The outcome of this process is a portable knowledge base, where the challenges lie in maintaining consistent knowledge, understanding the constraints inherent to a shell (the shell's ability to incorporate all relevant knowledge), and designing an acceptable user-expert system interface. The fundamental component of any expert system is its knowledge base. The issues to be presented in this symposium are important because they address three processes that are critical to the development of a knowledge base. In addition to presenting computer science challenges, knowledge base formulation also presents human factors challenges, for example, understanding cognitive problem-solving processes, representing uncertain information, and defining human-expert system interface problems. This symposium will provide a forum for discussion of both types of challenges.

An expert system is designed for determining substation locations and feeder configuration of a distribution system. By incorporating the heuristic rules currently followed by distribution engineers into the knowledge base, the expert system can benefit from system planners' experience in its problem-solving process. To minimize the feeder losses of the distribution plan, the location-allocation method is proposed for determining substation locations. The expert system is implemented using the artificial language PROLOG and is designed to be used interactively. The expert system is applied to the planning of a distribution system in Taiwan. Test results indicate that it is efficient and user friendly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The importance of operators' knowledge in the production of high-quality water and the need for consistent high-level expertise have fueled propositions for the application of computer technology, including knowledge-based expert systems (KBES), as a means of enhancing drinking water treatment operations. With this in mind, the authors have developed CHLOROS, a knowledge-based system which integrates both expert system and hypertext technologies in a WindowsTM environment. Designed as an off-line tool, the system does not directly control operations related to drinking water disinfection, but rather has been intended to assist treatment plant operations personnel. It is believed that CHLOROS will serve as a practical tool for water utilities because it creates a consistent decision-making environment, captures and preserves the expertise of human experts, and reduces the amount of manual effort and time that may be expended by searching through manuals and literature for information that is required to support the choice of control parameters.Key words: knowledge-based systems, expert system, hypertext system, disinfection, chlorination, chloramination.

In this paper, we examine the requirements for a self-contained, truly portable, vibration-measurement/real-time-analysis system with an integral computer which, with a resident expert system, can be used to diagnose the mechanical health of high-speed rotating machinery. The system is designed to be capable of providing rotating-equipment operators with the information they need to make timely decisions relative to the safe operation and maintenance of their machines. The basic system includes an automated vibration data-acquisition-and-reduction system, a machine-specific data base of vibration knowledge and an expert-system computer code which identifies, compares, trends and estimates the mechanical health of a turbomachinery system based on the past trends of the specific machine and the rules embedded in the expert system knowledge base. The machine-specific vibration data base stored in the hard-disk memory of the integral computer includes the frequencies, levels and patterns (signature spectra) of the synchronous, nonsynchronous, and subsynchronous frequency components associated with, say, the motion of the shafts, bearings and gears of a propulsion system and its auxiliaries. The “alarm” and “danger” vibration levels of these frequency components and encoded condition-specific diagnostic messages which characterize these abnormal vibration conditions are embedded in the system’s data base. The expert rules embedded in its knowledge base include diagnostic rules common to all rotating machines and a machine-specific knowledge base of vibration-response data which is being enlarged (within the limits of the computer memory) and improved continually. We describe the principal features of a prototypical system designed to accomplish the tasks of measurement and analysis cited above.

In this paper, the authors contend that the area of real estate pricing is often learned from an inefficient trial and error process. To address this problem, a knowledge-based expert system (REPRES) was developed by the authors. The knowledge base and inference engine logic of REPRES are described in the paper. Use of this expert system, in which the user is directed to the price of a given home using either a market or cost approach to pricing, should result in improved quality of real estate pricing, facilitate accumulation of further knowledge about the real estate pricing process, enhance communication between users of REPRES and customers, and assist in training individuals who are unfamiliar with the logic and knowledge base of real estate pricing. Validation results indicate REPRES has accomplished these goals. The authors conclude with a discussion of the implications of such an expert system to real estate pricing and real estate modeling research.

Safety evaluation system for hydraulic metal structures based on knowledge engineering

An expert advisory system for the ISO 9001 quality system

Knowledge-based Systems and Expert Systems, in particular, are expensive to build and difficult to validate and debug because of their complexity and dynamism. Therefore, it is not easy for knowledge engineer and domain expert to identify the gaps and mistakes in knowledge base. Unit testing is unable to cover validation process at all stages, in many cases manual thorough review of decision process is needed. In this paper we spot main approaches to validation and verification issue and describe a component that helps to debug a knowledge base by visualising execution of rules that derive a particular result. This component is developed for Knowledge-based Systems built on Drools Platform1 and we demonstrate application of this component in a knowledge-based engineering system for structural optical design.

Most of the CAE/CAD tasks are characterized as having large search spaces, because of this they have been resistive to traditional algorithmic approaches. In recent years the knowledge-based expert systems approach of Artificial Intelligence has promised a new solution. This tutorial provides: a background as to what are knowledge-based expert systems; the type of tasks where knowledge-based expert systems can be applied; and the process of building knowledge-based expert systems.

The need and desire to transform computers from machines that perform laborious, tedious and repetitive calculations to systems that can perform more intelligent tasks akin to human beings has been the single goal of the artificial intelligence (Al) community. With recent advances in the field of artificial intelligence this long awaited dream is becoming a reality. This success is due to what has become known as the knowledge-based expert systems technique. A characteristic of these systems is massive amount of knowledge in a specific domain which enables them to imitate the human problem solving process. Knowledge-based expert systems are able to perform tasks which are labeled as intelligent tasks when performed by human beings. More specifically, they are able to interpret data, monitor a system, diagnose a condition, plan an action, consult, reason, and explain their reasoning. These characteristics are the motivation for transforming expert systems from a few academic projects in the 1970’s to the explosive commercial market in the 1980’s. The importance of knowledge-based expert systems can be measured in several ways: the widespread application of these systems to various domains, a few in the 1970’s compared to 100s of applications in the 1980’s; the number of startup companies marketing knowledge-based expert systems and tools related to the development of these systems; the number of dollars spent by various governments and companies to achieve superiority and state of the art status in this field [Hayes-Roth 84].KeywordsExpert SystemVLSI DesignHuman DesignerProduction MemoryVLSI ChipThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.