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Purpose – The purpose of this paper is to provide a measurement system of knowledge‐based assets for graduate students, researchers and practitioners which can help them…
Purpose – The purpose of this paper is to provide a measurement system of knowledge‐based assets for graduate students, researchers and practitioners which can help them enhance their understanding of valuation issues. Design/methodology/approach – Three types of validity are reported to be relevant for the purposes of understanding knowledge‐based assets information systems: criterion validity – establishment of a statistical relationship with a knowledge‐based information system and productivity; content validity – representation of a specified universe of contents in the knowledge‐based information system; construct validity – measurement of knowledge. Findings – A framework is provided that helps explain why measurement is important in deciding characteristics such as information value, cost, reliability, validity, and bias (random and non‐random error) which is germane to the development of an efficient and effective knowledge‐based assets information system. Practical implications – The paper is a very useful source of information for graduate students, researchers and practitioners involved with testing, designing, valuing and/or implementing a knowledge‐based information system. Originality/value – A measurement model is presented that may spark future models that can be implemented, tested and translated into actions in various organizational settings.
Decision making in Flexible Manufacturing Systems (FMS) isdifficult because of their high complexity level. The operational levelof FMS is concerned with the detailed…
Decision making in Flexible Manufacturing Systems (FMS) is difficult because of their high complexity level. The operational level of FMS is concerned with the detailed decision making required for real‐time operation. This applies to various control problems such as selection of a transportation path to move parts between stations. Describes a prototype knowledge‐based system for selection of a transport path in real‐time control of FMS. The knowledge‐based system is evaluated with an empirical approach.
The application of information technology (IT) to corporateenvironments is now widespread. Recent years have seen IT successfullyapplied to a wide range of corporate…
The application of information technology (IT) to corporate environments is now widespread. Recent years have seen IT successfully applied to a wide range of corporate activities. Artificial intelligence, in the guise of knowledge‐based systems is on the point of delivering its long awaited potential. With many successful systems developed over the past three or four years, the time is now ripe for the corporation to develop a coherent strategy to investigate and exploit this technology. In this, the second of two articles looking at knowledge‐based systems, we examine how the corporation can exploit this technology for strategic and competitive advantage. It discusses the organisational implications of KBS as well as the impact and pay‐off that can be expected.
As it becomes increasingly evident that skillsshortage in many areas of business and commerceis likely to grow, it is clear that knowledge‐basedsystems can go some way…
As it becomes increasingly evident that skills shortage in many areas of business and commerce is likely to grow, it is clear that knowledge‐based systems can go some way towards replacing human resources. The use of these systems also increases the productivity and effectiveness of already experienced personnel and also brings with it consistent standards of performance and reliability. A better balance is also achievable between the tasks done by employees and those performed by machines.
The present paper reports on the development of SITE EXPERT: a prototype knowledge‐based expert system. It is an advisory system. SITE EXPERT is intended to be used for…
The present paper reports on the development of SITE EXPERT: a prototype knowledge‐based expert system. It is an advisory system. SITE EXPERT is intended to be used for productivity improvement in construction and provides advice on: (1) the productivity of three basic operations of construction, i.e. pouring and placing of concrete, erection and removal of formwork, and fixing reinforcement; and (2) human resources and site layout as productivity factors. The system uses information from construction experts, text books, data recorded at construction sites and the engineer's own knowledge, as well as knowledge obtained by running simulation models. In the present paper, the development, operation and evaluation of the prototype system is described. The results of this prototype system development demonstrate that artificial intelligence methodologies provide powerful facilities for capturing information about construction processes and advising the practitioners of construction on productivity improvement within a computer format close to human reasoning.
The goal of this paper is to re‐evaluate the role of knowledge‐based systems (KBS) in knowledge management (KM). While knowledge‐based systems and expert systems were…
The goal of this paper is to re‐evaluate the role of knowledge‐based systems (KBS) in knowledge management (KM). While knowledge‐based systems and expert systems were widely used in the past, they have now fallen from favor and are largely ignored in the knowledge management literature. This paper aims to argue that several factors have changed and it is now time to re‐evaluate the contribution that such systems can make to knowledge management.
The role of KBS in KM is explored through a comprehensive analysis of both the management and the technical literature on knowledge. The literature on KBS and expert systems is reviewed and some of the problems faced by them are highlighted. Some of the probable causes of these problems and some of the solutions that might be used to overcome them are indicated. The paper describes how knowledge systems (KS) could be used as an effective tool for managing knowledge.
The lack of success of KBS technologies for managing knowledge is mainly due to organizational and managerial issues. These problems can be solved through feasibility studies before system development activities. KS technology is now being successfully applied in a variety of newer domains that exploit its capabilities.
Some conclusions are drawn concerning integration of knowledge systems with knowledge management, problems of the early implementation of knowledge systems technology, and possible solution to overcome these problems.
The main contribution of the article is in re‐evaluating the role of knowledge‐based systems as a tool for knowledge management.
Knowledge‐based systems, especially so‐called expert systems, which replicate the problem‐solving or decision‐making capabilities of human experts in specific areas, have…
Knowledge‐based systems, especially so‐called expert systems, which replicate the problem‐solving or decision‐making capabilities of human experts in specific areas, have recently gained considerable widespread interest. The advent of such systems emphasizes the critical role of employee competence, skills, knowledge and experience in an organization. This paper presents the salient features of knowledge‐based computing systems in modern office environments. In particular, I consider topics related to critiquing consultation systems and the possibilities of re‐using knowledge bases for training. The paper also discusses possible consequences, benefits, problems and other important issues in the area.
Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid…
Analyzing current recommender systems, it is observed that the cold start problem is still too far away to be satisfactorily solved. This paper aims to present a hybrid recommender system which uses a knowledge‐based recommendation model to provide good cold start recommendations.
Hybridizing a collaborative system and a knowledge‐based system, which uses incomplete preference relations means that the cold start problem is solved. The management of customers' preferences, necessities and perceptions implies uncertainty. To manage such an uncertainty, this information has been modeled by means of the fuzzy linguistic approach.
The use of linguistic information provides flexibility, usability and facilitates the management of uncertainty in the computation of recommendations, and the use of incomplete preference relations in knowledge‐based recommender systems improves the performance in those situations when collaborative models do not work properly.
Collaborative recommender systems have been successfully applied in many situations, but when the information is scarce such systems do not provide good recommendations.
A linguistic hybrid recommendation model to solve the cold start problem and provide good recommendations in any situation is presented and then applied to a recommender system for restaurants.
Current recommender systems have limitations in providing successful recommendations mainly related to information scarcity, such as the cold start. The use of incomplete preference relations can improve these limitations, providing successful results in such situations.
Knowledge‐based systems are beginning to provide executives with powerful systems which serve to automate corporate expertise. This is the first of two articles exploring…
Knowledge‐based systems are beginning to provide executives with powerful systems which serve to automate corporate expertise. This is the first of two articles exploring KBS and their corporate implications.
Knowledge‐based systems have been successfully utilised in the develop‐ment of complex systems. In many cases, these systems have emphasised the need for techniques to…
Knowledge‐based systems have been successfully utilised in the develop‐ment of complex systems. In many cases, these systems have emphasised the need for techniques to integrate knowledge‐based processing with methods for managing both large amounts of data and knowledge. However, many potential applications for expert systems are precluded by limitations in the ability of conventional expert system technology to function in conjunction with data systems without manual intervention. The author focuses on the integration of knowledge‐bases and databases with the capability to: store and context select between parallel, competing expert system rule structures; cascade variable rule structures; allow an expert system to be interrupted and to be subsequently restarted by storing the state of the inference engine; handle simple data storage and retrieval.