Introduction of knowledge bases in patient's data management system: Role of the user interface
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Aiming at problems of traditional knowledge representation in the machining dosage expert system, this paper applies data - base technology to it using object - oriented technique. A knowledge representation frame model is established which combined the repository and the data - base. After used at actual expert system, the model gets knowledge from the data - base and makes knowledge representation easier.
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Abstract Solutions to many problems in AI depend more on the availability of a large amount of knowledge than on sophisticated algorithms. Knowledge representation is the study of ways of supplying programs with such knowledge. The term “knowledge base” is used to refer to the body of knowledge made available to the program. The two main parts of any AI system are a knowledge base and an inferencing system. The specification of a knowledge representation consists of two major components: A description of the notation used to express facts and a description of the operations that can be performed on a knowledge base. A notation in which facts can be expressed is essential to any knowledge representation. To build the knowledge base, a variety of knowledge representation schemes are used including logic, lists, semantic networks, frames, scripts, and production rules. Although there is little agreement as to what knowledge representation actually is, many schemes have been proposed as general frameworks for representing and storing knowledge. Some have been successfully used as a basis for working systems. There are, however, many features of knowledge such as defaults that are not well understood. Until a better understanding of these features is reached, knowledge representation will remain an active area of study.
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So far, many knowledge representation methods have been discussed. Each method has its own advantages and disadvantages, so it is not possible to introduce a specific method as the best knowledge representation technique. We introduce a unique knowledge base, E/sub x/-OAV KB. As the verification and validation (V&V) of knowledge in any AI systems is important, we propose a new user friendly tool, TKT-OAV, to transform the knowledge representation method of any expert system such as semantic network or frame to the E/sub x/-OAV KB. By using this tool, we could also draw and edit the semantic network or frame and construct the knowledge base. This tool uses the standard algorithms that have been designed by C. Dadkhah and A. Ahmad (2004) for transforming the knowledges. The transformed knowledges are shown in three forms: OAV triple, OAV hierarchy and OAV table. Finally the knowledge base which its knowledges are represented in OAV triple is created. Any expert system shell can use this KB for reasoning and the verification and validation tools like OAV/spl I.bar/VVT expert, could also verify and validate this knowledge base.
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In current society knowledge, information and intelligent computer systems based on knowledge base play a great role. The ability of an intelligent system to efficiently implement its functions depends on the efficiency of organizing knowledge base, and on the fact whether the applied knowledge representation models comply with the set requirements. The article is devoted to the research of the problem of choosing the knowledge representation models. Based on the requirement analysis for knowledge representation models, one of the solutions for the researched problem shown is application of extended semantic networks. Analysis of extended semantic networks' properties is carried out, as well as relevant examples of representing knowledge of extended semantic networks' application for various spheres offered.
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This paper describes an approach to integrate knowledge base via converting predicates into Semantic networks and in frames. A knowledge base can be represented in a tabular form, a rule form, a tree form or any other form suitable for knowledge representation. Form conversion can be accomplished at all times. Unification of knowledge always overcome individual limitations and has synergetic effects in knowledge extraction. The graphical representation of knowledge base has more understandability than any other representation. Aim of this paper is to develop a system which accepts input from the user in the form of predicates and generates outputs with graphical representation of semantic networks as well as of frames.
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When we develop and expert system, knowledge bases which were built individually can be merged easily or connected organically, and can be used effectively. We have developed a total management system for knowledge bases based on frame representation. This paper describes a method by which to represent knowledge for the mechanical engineering field and by which to manage knowledge bases with different knowledge representation, and emphasizes the effectiveness of extended functions of frame representation, system slot and inheritance. As examples, using this management system, we show the connection of an existent CAD system and an existent program "synchronized meter".
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It is possible to incorporate a system which manages a knowledge base into an expert system. Because, by using this system, we can rebuild knowledge bases which have no logical contradictions with knowledge of experts and phenomena which we experience daily. However, it is difficult to build beyond systems in the machining field due to the existence of many kinds of knowledge, if we want to achieve rebuilding of the knowledge bases using current representation language. Thus, we developed a truth maintenance system for knowledge bases by using a new predicate-logic representation language. This paper describes how to manage knowledge bases and how to treat the functions in a truth maintenance system using this system when contradictions occur in the practical machining problem.
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In contrast to conventional database systems, AI systems require a knowledge base with diverse kinds of knowledge. These include, but are not limited to, knowledge about objects, knowledge about processes, and hard-to-represent commonsense knowledge about goals, motivation, causality, time, actions, etc. Attempts to represent this breadth of knowledge raise many questions: (1) How do we structure the explicit knowledge in a knowledge base? (2) How do we encode rules for manipulating a knowledge base's explicit knowledge to infer knowledge contained implicitly within the knowledge base? (3) When do we undertake and how do we control such inferences? (4) How do we formally specify the semantics of a knowledge base? (5) How do we deal with incomplete knowledge? (6) How do we extract the knowledge of an expert to initially "stock" the knowledge base? (7) How do we automatically acquire new knowledge as time goes on so that the knowledge base can be kept current? This special issue introduces this important area of artificial intelligence to a wider audience. The core of the 15 articles, contributed by a broad spectrum of researchers on various aspects of knowledge representation, show the importance, diversity, and vigor of knowledge representation as a research activity. This introduction provides some background and context to these articles by mapping out the basic approaches to knowledge representation that have developed over the years.
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