An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

A knowledge sorting process for a product design expert system

Knowledge sorting is one way to organize the knowledge acquired from the domain expert(s) and various sources. Well-organized knowledge representation will make rule extraction much easier. This paper describes a knowledge sorting process that was developed to facilitate the rule extraction for a product design expert system. The process capitalizes on the relationships between design attributes and factors, dependent and independent variables, and consists of three stages: identification of knowledge sources, generation of taxonomic trees, and organization of acquired knowledge. An example applies the sorting process to the development of an expert system for the design of wood head golf clubs.     

面向对象知识表示在起重机专家系统中的应用

知识表示是专家系统的关键组成部分。在对知识表示的方法进行探究的基础上,根据起重机操作专家系统的特点,采用面向对象与产生式表示方法相结合的混合表示方法对其专家知识进行表示,并在C＋＋程序语言环境下实现起童机操作知识的表示,同时给出起重机操作的知识表示实例,用此法开发的专家系统具有代码短、易维护、易修改和易扩充的优点。阐述了起重机操作专家系统如何用面向对象方法封装规则、建立知识库、实现知识推理和具体的应用要点。     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

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.     

A comparison of knowledge elicitation methods

This paper describes a computer based approach to comparing data obtained for Knowledge Based Systems via established but very varied knowledge elicitation (KE) techniques. It describes not only the detailed comparison of different KE methods (in this case 'scaling' and 'non-scaling') but also investigates the use of 'demonstration' or 'evaluation' systems, as a variation on the more established rapid prototyping approaches to the elicitation and evaluation of knowledge for KBS construction, in this case by focusing upon the quality and relevance of the elicited knowledge from the perspective of the expert himself. Preliminary results from the study reported here suggest that non-scaling methods produce a greater amount of raw data than scaling methods, and that this data is less likely to require correction or modification for inclusion within a Knowledge Based System. However, the results also indicate that non-scaling derived data is more likely than scaling derived data data to be rejected outright.     

基于LOM的知识模型与个性化学习控制

在智能教学系统中,知识模型是专家模型的核心,是实现智能教学的一个关键问题。本文讨论知识点及其相互关系的形式表示,提出基于LOM的知识点模型与学习控制策略。知识点被设计、封装为学习对象,提高了学习内容的可重用性、可管理性、可定制性与互操作性。LOM的语义描述能力,使得系统本身具有一定程度的知识理解能力。以知识点LOM模型为结点的知识网络是语义网络,其语义计算能力便于知识的发现与关联。将专家知识网络动态映射为学生知识网络,实现了个性化学习。     

A knowledge-tracing model of learning from a social tagging system

We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system. Tags identified with “expertise” in a domain can be used to identify a corpus of domain documents. That corpus can be fed to an automated process that distills a topic model representation characteristic of the domain. As a learner navigates and reads online material, inferences can be made about the degree to which topics in the target domain have been learned. We validate this knowledge tracing approach against data from a social tagging study. As part of this evaluation, we match the predictions of the knowledge-tracing model to individual participant responses made to individual question items used to test domain knowledge.     

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.     

行业信息化知识库系统知识库设计

行业信息化知识库(KBI)有别于一般的知识库和专家系统,所以根据需求,采用了知识本体的知识表示形式,为了更合理的建设知识库,通过对行业信息化知识的行业结构分析,构建了知识模型,并依据知识表示形式以及构建的知识模型,设计了行业信息化知识库系统的知识库.该知识库是针对战略物资行业的,提出了知识库系统的总体结构,把知识库和数据库相结合,进行了知识库结构的逐步设计.     

信息系统开发中的知识转移研究综述

知识管理已经成为信息系统领域的研究热点,其中一个重点就是知识转移。论文对信息系统开发过程中知识转移研究文献从因素研究、开发方法、外包、动态知识流、知识分离与集成等方面进行了综述,分析了该领域的研究现状、存在的问题。未来应从知识管理的视角研究ISD开发过程,把需求分析等活动上升到知识分析的高度,使知识管理研究贯穿信息系统的整个生命周期中,及时全面地发现用户深层次的需求,为开发过程改进提供理论基础。     

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.     

Concept classifier for knowledge acquisition in frame-based systems

Frame-based systems that employ inheritance networks as a form of knowledge representation have a number of inherent knowledge acquisition problems, one of the most significant being the transfer to the representation system of knowledge itself. The problem of concept classification, and specifically that of determining the location of a new concept in an existing network inheritance hierarchy, is discussed here using an experimental knowledge-base editor, KRE. Tools which support the process of knowledge base construction must allow the user to concentrate on the domain problems, and not on low level, representation system decisions. KRE, written in C, is a knowledge acquisition tool which assists the knowledge engineer by using an interactive acquisition strategy during the process of concept classification. The processes of classification, and its advantages over other knowledge representation systems, are presented.     

SEWEBAR-CMS: semantic analytical report authoring for data mining results

SEWEBAR-CMS is a set of extensions for the Joomla! Content Management System (CMS) that extends it with functionality required to serve as a communication platform between the data analyst, domain expert and the report user. SEWEBAR-CMS integrates with existing data mining software through PMML. Background knowledge is entered via a web-based elicitation interface and is preserved in documents conforming to the proposed Background Knowledge Exchange Format (BKEF) specification. SEWEBAR-CMS offers web service integration with semantic knowledge bases, into which PMML and BKEF data are stored. Combining domain knowledge and mining model visualizations with results of queries against the knowledge base, the data analyst conveys the results of the mining through a semi-automatically generated textual analytical report to the end user. The paper demonstrates the use of SEWEBAR-CMS on a real-world task from the cardiological domain and presents a user study showing that the proposed report authoring support leads to a statistically significant decrease in the time needed to author the analytical report.     

ON KNOWLEDGE REPRESENTATION FOR INFERENCES IN CAST METHOD BANKS

This article explains why aspects of knowledge representation must be considered in the context of computer aided systems theory (CAST). CAST method banks support human experts during the process of problem solving. They should be understood as decision support systems, as assistants of their human expert users. One key to making this approach work is the communication between the expert and the system. The assistant should provide systematical and goal-directive information about the current problem state for the human expert. Another, even more important requirement is the assistant's knowledge about all available methods at a certain problem-solving state and their expected impact on the further problem-solving process. Knowledge representation denotes how the problem domain is represented within the support system and how it is used. We investigate different forms of knowledge representations and summarize criteria for the applicability of different forms of knowledge representations in CAST systems.     

基于规则的联想网络

本文提出一种用于人工智能专家系统的知识表达方式--基于规则的联想网络(ANBR), ANBR把智能系统获取的知识表达分为内外两种形式.外部形式是基于规则的知识结构,用 于知识的获取和知识库管理;内部形式是分块联想网络,用于驱动推理控制.由于把产生式规 则的可理解性和模块性与网络的知识索引和联想推理功能结合于同一系统,使ANBR成为专 家系统知识表达的有效方式.     

Brussels revisited: The European conflicts guide

In this paper, we examine the soundness of Capper & Susskind's recommended legal expert system development methodology in the areas of knowledge acquisition and knowledge representation. Legal expert systems have not yet had the impact in the United Kingdom that might be expected. We argue that this is partly the result of developers paying insufficient attention to the ‘third estate’: user interfaces. We make suggestions about both the look and feel of legal expert systems, and the facilities that such systems should offer. Lastly, we claim that we have developed an exploratory expert system encapsulating the Brussels Convention 1968 which can contribute to the development of a useful computer‐based guide to an important legal domain.     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

知识工程中的知识库、本体与专家系统

随着语义Web思想的兴起,对专家系统的互操作性和共享性也提出了更高的要求,对新型知识表示方式和新型知识系统的研究是大势所趋.本体作为共享概念模型的明确的形式化规范说明,为不同系统之间的互操作提供了基本的框架,是解决共享和互操作问题的有效的方法.一些初学者对知识库、本体以及专家系统的概念产生了各种疑惑,根据笔者的理解与实践,对知识工程中的本体、知识库以及专家系统做出较系统的比较分析,对这几个术语做一个澄清.