Knowledge-based systems from a socio-cognitive 1 perspective

Abstract

Recent trends in the design and development of knowledge-based systems KBSs are discussed with special emphasis on issues that relate to situated knowledge. A knowledge base is regarded as a model of expertise that acknowledges the embeddedness of expert knowledge in social interaction and in the workplace in general. KBS development is viewed as an instance of socio-technical design. Experience from several European projects is recounted to illustrate the issues addressed. Suggestions for KBS development are presented as methodological guidelines, with special emphasis on systems employing case-based reasoning.     

Knowledge-based systems and knowledge management: Friends or foes?

Knowledge-based systems (KBS) provide a way of formalizing and automating knowledge. Their worth for managing the knowledge assets has not gone unnoticed: they have been promoted as safeguards to retain expert knowledge, to avoid knowledge erosion, etc. KBS are the outcome of a knowledge engineering process that may be seen as providing some of the building blocks of knowledge management. Although `knowledge' is the first word in knowledge-based systems, they are hardly ever considered from a knowledge perspective. As a result, a biased view of the organizational value of KBS exists in the literature, putting an undue emphasis on technology. The key issue addressed in this article is: how does knowledge engineering relate to a broader perspective of knowledge management? A way to identify the issues to be addressed when valuing KBS as potential measures for knowledge management is presented. To illustrate its value, the outcomes of a recent empirical investigation of how KBS function within organizations are presented.     

Some technical and non‐technical issues in implementing a knowledge server

Increasingly, knowledge, as well as information and data, is being transferred over the World Wide Web. There is great potential in linking traditional knowledge‐based systems (KBS) technology with the Internet because each technology can overcome limitations in the other. As a result, it might enable expert knowledge that has hitherto been confined to those who possess the correct computing platforms to be made available to small enterprises and people in developing countries. Five types of KBS–Internet integration are outlined (Intelligent Agents, Active Web Pages, Local KBS Accessing Web‐distributed Information, Web‐distributed Knowledge Bases, and Knowledge Servers). This paper discusses knowledge servers in detail. It examines the issues and problems that must be addressed if existing KBS inference software is to be integrated with the World Wide Web, and discusses, in depth, solutions as implemented in the Istar knowledge server. The paper shows how technical design and implementation decisions can be influenced, not only by the technical characteristics of the Internet, but also by a range of other, ‘softer’ issues. In particular, it shows how real life styles of WWW browsing, and a desire to make knowledge available to developing countries, influences both overall architecture and detailed implementation decisions. Early experience of actual usage shows Istar to be a highly efficient knowledge server. Directions for future research are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

Modeling in the design of a KBS validation system

We describe and illustrate the modeling issues in the design of a system for validation of knowledge based systems (KBSs). the domain of such a validation system is “KBSs and their validation problems.” the basic idea in our solution is the following. Since different KBSs may use different knowledge representation languages, we first represent the target KBS (i.e., the KBS to be validated) in a general formal model of KBS, and then validate it in this form. the advantage of this strategy is that validation problem solving needs only to refer to the common language of the general formal model. We present a set of possible conceptual abstraction levels in such a model, and argue that each level is associated with a related view on validation problems. Since high level characterizations are difficult to abstract from current knowledge representation languages, we consider the formal aspects of modeling mainly at the “lowest” level, the so-called inference primitive level. We illustrate the approach by formalizing a solution for selected modeling issues at this level.     

Convergence and divergence in information systems and knowledge based systems development methodologies: a case for integrated strategic planning

There is a need to integrate knowledge based systems (KBS) with information systems (IS) technical solutions, which implies that KBS and IS development methodologies should be less isolated from each other. KBS and IS development methodologies are generally examined in terms of their similarities and differences. There is divergence at the feasibility and analysis stages, convergence at the design and coding stages, divergence during testing, convergence at the implementation stage, and divergence during maintenance. There are more similarities than there are differences between IS and KBS methodologies, particularly during the strategic planning stage. It is argued that linking KBS strategic planning to the planning element of an IS methodology will go further towards ensuring that the whole of the business is considered, leading to better integrated IS/KBS solutions. Application selection activities of two well-known KBS methodologies are briefly analysed. We show that these activities, which precede the feasibility study, do not consider the strategic aspects of the use of KBS (and IS in general) in business organizations. It is argued that a KBS strategy should be formulated in relation to the IS strategy and the business strategy, therefore increasing the convergence between IS and KBS methodologies.     

Knowledge-based systems in civil engineering: Three case studies

Although substantial advances have been achieved in last twenty years, in practice we are still far from taking full advantage of the potential of knowledge-based systems (KBS). Very few KBS have survived their evaluation period and only one third of those that were initially reported as successful are still in use. The primary purpose of the paper is to report on three KBS applications that have been developed for research purposes in the field of civil engineering. The main challenge underlying all three projects has been to develop computer support systems that would induce changes and improvements to the way that engineers solve their everyday problems. A great deal of effort has been put into eliciting knowledge and reasoning strategies from engineering experts with the aim of building up a computer model of their expertise in order to assist engineers in their decision-making processes. The paper closes by highlighting the principal achievements and the main issues concerning the future development of KBS for solving real life problems.     

The importance of detailed evaluation for KBS implementation in the engineering industry

The evaluation of a knowledge based system(s) (KBS) is discussed, with particular relevance to the development of KBS intended for implementation in the engineering industry.This discussion is based on the practical experiences of the authors when evaluating a KBS for use in the conceptual design of bridges. Consequently, detailed references are made to the actual evaluation carried out and the results found.The way in which systems have previously been evaluated is also discussed. The deficiencies of these past evaluation techniques are identified and ways in which the evaluation process could be improved are suggested.     

A set of design guidelines for object-oriented deductive systems

This paper presents a set of design guidelines for the construction of complex, real-world problem-solving systems using a hybrid object-oriented deductive formalism. These guidelines address implementation issues in a multi-paradigm environment. Examples are provided in the context of a large-scale knowledge based system (KBS)     

An organizational approach to designing an intelligent knowledge-based system: Application to the decision-making process in design projects

Knowledge-based engineering (KBE) approaches are designed to reduce the time and cost of product development by capturing, retaining and re-using design knowledge. They currently focus on repetitive design tasks where knowledge is considered as a static resource. However, knowledge is intrinsically linked to the organizations and people who use it. Thus, to be efficient, these knowledge-based systems (KBS) have to be able to take into account all the mechanisms of knowledge creation, sharing and evaluation made by the users. Using the agent paradigm, new knowledge-based systems can be designed in order to address this research issue. Indeed, the agents have social abilities and are able to achieve very complex tasks. These two features are necessary for making a knowledge-based system efficient. However, there still exists today a lack of approaches and methodologies to help design such applications. This paper presents DOCK, a methodology to design an intelligent knowledge-based system that aims to support the knowledge management process. In order to take into account all the mechanisms of knowledge generation, sharing and re-use, DOCK is based on the hypothesis that efficient modelling of human organizations, by highlighting their roles, collaborations, skills, goals and knowledge, will help the KBS designer to specify an adapted knowledge-based system. Finally, DOCK is implemented to design the SMA SNOTRA that is dedicated to supporting a decision-making process for design projects.     

The organisational impact of knowledge-based systems: a knowledge perspective

The fact that knowledge-based systems (KBS) may have considerable impact when introduced into an organisation is beyond dispute. The assessments of this impact in the literature, however, are not satisfactory. They overlook the main discriminating characteristic of KBS, i.e. the fact that KBS claim to store and handle knowledge. The article explores ways for bringing ‘knowledge’ into discussions of the impact of KBS. A knowledge perspective is developed which does justice to the impact of KBS on both articulated and tacit knowledge at the strategic, tactical and operational level. Possible applications of this perspective are explored with illustrations from an empirical investigation of KBS in 17 organisations.     

基于XML与Web的产品设计知识表示与知识库系统

提出了一种新型的知识库系统。首先提出一种面向产品设计推理的语义关系标记集,该标记集可消除知识语句的歧义并保持原文的自然语言顺序,并为该语义标记集设计相应的XML标记。其次,提出知识文档标记方法、基于XML的概念及概念词典（本体）的定义方法。最后,开发新型知识库系统的原型系统,系统的运用显示新型知识库更易实现基于网络共享与维护。     

Recent development of knowledge-based systems,methods and tools for One-of-a-Kind Production

In recent years, product knowledge has played increasingly significant roles in new product development process especially in the development of One-of-a-Kind products. Although knowledge-based systems (KBSs) have been proposed to support product development activities and new knowledge modelling methodologies have been developed, they are still far from complete. This area has become attractive to many researchers and as a result, many new knowledge-based systems, methods and tools have been developed. However, to the best of our knowledge, knowledge-based systems for product development have not been systematically reviewed, compared and summarized. This paper provides a comprehensive review on the recent development of KBS, methods and tools in supporting rapid product development. In the paper, the relevant technologies for modelling, managing and representing knowledge are investigated and reviewed systematically for better understanding their characteristics. The focus is placed on knowledge-based systems that support product development, and how product knowledge is identified, captured, represented and reused during the processes of One-of-a-Kind product development. The limitations and the future trend of KBS are presented in terms of how they can help One-of-a-Kind Production (OKP) companies.     

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

Abstract

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.     

ISTAR — a tool for creative design of knowledge bases

This paper gives a comprehensive explanation of the Istar knowledge representation software tool. Not only does it describe the features and facilities found in Istar, but it discusses why they are as they are.
Istar is one of a new generation of knowledge representation tools, aimed at ill-structured domains of knowledge. While it can be used in traditional KBS projects, in which pieces of knowledge from a domain expert are assembled to form a working knowledge base, it is designed for situations in which there is a large element of creative design: knowledge refinement and generation resulting from the knowledge representation process.
The knowledge representation 'language' is purely graphical; the knowledge engineer 'draws' knowledge on an easel as a box and arrows diagram. Behind this diagram is the knowledge base itself, in the form of integrated inference nets, Bayesian nets and semantic nets. This paper discusses the reasons for these design choices and, briefly, some of the issues faced in development of Istar.     

State-of-the-practice in knowledge-based system verification and validation

The majority of the work in knowledge-based system verification and validation (KBS V&V) has focused on developing techniques and concepts for performing V&V on expert systems. Little information is available on what V&V practices are currently in use by expert system developers. This article describes the results of a survey whose purpose was to begin documenting some of the experiences and problems KBS developers have encountered. The results of the survey suggest that current practices can be improved and some specific recommendations are included. The recommendations include developing requirements for expert system V&V, addressing common issues, recommending a life cycle for expert systems development, addressing readability and modularity issues, and investigating applicability of analysis tools.     

Experience with Ripple-Down Rules

Ripple-Down Rules (RDR) is an approach to building knowledge-based systems (KBS) incrementally, while the KBS is in routine use. Domain experts build rules as a minor extension to their normal duties, and are able to keep refining rules as KBS requirements evolve. Commercial RDR systems are now used routinely in some Chemical Pathology laboratories to provide interpretative comments to assist clinicians make the best use of laboratory reports. This paper presents usage data from one laboratory where, over a 29 month period, over 16,000 rules were added and 6,000,000 cases interpreted. The clearest evidence that this facility is highly valuable to the laboratory is the on-going addition of new knowledge bases and refinement of existing knowledge bases by the chemical pathologists.     

Expert system application on preliminary design of water retaining structures

Design of liquid retaining structures involves many decisions to be made by the designer based on rules of thumb, heuristics, judgment, code of practice and previous experience. Various design parameters to be chosen include configuration, material, loading, etc. A novice engineer may face many difficulties in the design process. Recent developments in artificial intelligence and emerging field of knowledge-based system (KBS) have made widespread applications in different fields. However, no attempt has been made to apply this intelligent system to the design of liquid retaining structures. The objective of this study is, thus, to develop a KBS that has the ability to assist engineers in the preliminary design of liquid retaining structures. Moreover, it can provide expert advice to the user in selection of design criteria, design parameters and optimum configuration based on minimum cost. The development of a prototype KBS for the design of liquid retaining structures (LIQUID), using blackboard architecture with hybrid knowledge representation techniques including production rule system and object-oriented approach, is presented in this paper. An expert system shell, Visual Rule Studio, is employed to facilitate the development of this prototype system.     

A tool-supported approach to inter-tabular verification

The use of decision tables to verify knowledge based systems (KBS) has been advocated several times in the validation and verification (V&V) literature. However, one of the main drawbacks of these systems is that they fail to detect anomalies that occur over rule chains. In a decision table based context this means that anomalies that occur due to interactions between tables are neglected. These anomalies are called inter-tabular anomalies. In this paper we investigate an approach that deals with inter-tabular anomalies. One of the prerequisites for the approach was that it could be used by the knowledge engineer during the development of the KBS. This requires that the anomaly check can be performed on-line. As a result, the approach partly uses heuristics where exhaustive checks would be too inefficient. All detection facilities that will be described have been implemented in a table-based development tool called . The use of this tool will be briefly illustrated. In addition, some experiences in verifying large knowledge bases are discussed.     

Engineering materials information systems

Materials properties information systems are poorly understood. Many databases of materials properties and designations have been produced but, except in the most modest of cases, they have been less successful than their creators had hopedKnowledge based systems (KBS) are subject to exactly the same problems as data based materials information systems and it is important to realize what these are before the special character of KBSs can be used to alleviate them.This paper surveys the unusual and difficult aspects of engineering materials information that must be handled by any organizing methodology, whether manual or computerized, data based or knowledge based, handling information which is stored or inferred.