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Journal articles on the topic 'Knowledge based sysems'

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Author: Grafiati

Published: 10 December 2022

Last updated: 19 February 2023

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1

Loudon, T. Victor. "Wissensbasierte Systeme und geologische Feldaufnahme." Zeitschrift der Deutschen Geologischen Gesellschaft 155, no. 2-4 (June 1, 2005): 225–46. http://dx.doi.org/10.1127/zdgg/155/2005/225.

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2

Kovalenko, O. E. "Knowledge based design of convergent systems of situational management." Mathematical machines and systems 3 (2019): 67–74. http://dx.doi.org/10.34121/1028-9763-2019-3-67-74.

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3

Havlíček, J., J. Hron, and I. Tichá. "Knowledge based higher education." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (February 17, 2012): 107–16. http://dx.doi.org/10.17221/5002-agricecon.

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While data and/or information based education was built on pedagogic, psychology, philosophy of science and didactic disciplines, the new dimension of knowledge based education will involve new disciplines such as Knowledge Management, Epistemology, Systems Theory, Artificial Knowledge Management Systems, Value Theory and Theory of Measurement. It is often assumed that data, information and knowledge are depicted as a pyramid. The data, the most plentiful type, are at the bottom, information, produced from data, is above it and knowledge, produced from information through the hard work of refining or mining, above it. This schema satisfies specific needs of an organisation of warehouse data systems but it does not explain the role of these objects in the educational process. In education, the distinctions among data, information and knowledge need to be distinguished from the complex pedagogical point of view. Knowledge is the engine asking for more information and more data. Knowledge life cycle produces more information, more information asks for more data – that is: there is “just information”. Data, information and knowledge can be considered as object oriented measures assigned to real objects (entities). The following measures can be assigned to the objects: Measure of the zero order – name. Measure of the first order – data. Measure of the second order – information. Metrics of the third order – knowledge. Knowledge based curriculum involves knowledge into study plans and it considers knowledge as a distinctive part of study. Knowledge becomes the engine starting cycle of new information acquisition, reproduction and integration. The following problems have to be solved in building of knowledge based curriculum: Methodology and organisation of educational process. Technical support for knowledge based education. Evaluation and assessment of the process.

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4

Jahanshiri, E., and S. Walker. "Agricultural Knowledge-Based Systems at the Age of Semantic Technologies." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 64–67. http://dx.doi.org/10.7763/ijke.2015.v1.11.

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5

Buchanan, Bruce G., Daniel Bobrow, Randall Davis, John McDermott, and Edward H. Shortliffe. "Knowledge-Based Systems." Annual Review of Computer Science 4, no. 1 (June 1990): 395–416. http://dx.doi.org/10.1146/annurev.cs.04.060190.002143.

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6

Kahn, M. G. "Knowledge-Based Systems." Yearbook of Medical Informatics 02, no. 01 (August 1993): 399–401. http://dx.doi.org/10.1055/s-0038-1637984.

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7

Mainzer, Klaus. "Knowledge-based systems." Journal for General Philosophy of Science 21, no. 1 (March 1990): 47–74. http://dx.doi.org/10.1007/bf01801416.

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8

DAVIS, R. "Knowledge-Based Systems." Science 231, no. 4741 (February 28, 1986): 957–63. http://dx.doi.org/10.1126/science.231.4741.957.

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9

Motoda, H., R. Mizoguchi, J. Boose, and B. Gaines. "Knowledge acquisition for knowledge-based systems." IEEE Expert 6, no. 4 (August 1991): 53–64. http://dx.doi.org/10.1109/64.85921.

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10

Müller-Kolck, Ulrich. "Knowledge acquisition for knowledge-based systems." Artificial Intelligence in Medicine 2, no. 2 (June 1990): 119–20. http://dx.doi.org/10.1016/0933-3657(90)90033-n.

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11

Choochaiwattana, Worasit. "A Comparison between Item-Based and Tag-Based Recommendation on a Knowledge Management System: A Preliminary Investigation." International Journal of Information and Education Technology 5, no. 10 (2015): 754–57. http://dx.doi.org/10.7763/ijiet.2015.v5.605.

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12

Weitzel, J. R., and L. Kerschberg. "A system development methodology for knowledge-based systems." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 3 (1989): 598–605. http://dx.doi.org/10.1109/21.31065.

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13

CASTAÑO, CRISTIAN, GERALDIN QUINTERO, and EDGAR DUARTE. "SISTEMA DE GESTIÓN DEL CONOCIMIENTO BASADO EN ESTÁNDARES DE ALTA CALIDAD PARA UN PROGRAMA EDUCACIÓN SUPERIOR." Revista Ingeniería, Matemáticas y Ciencias de la Información 8, no. 16 (July 31, 2021): 55–64. http://dx.doi.org/10.21017/rimci.2021.v8.n16.a102.

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Knowledge management is a useful tool for the competitiveness of many organizations. The higher education sector is particularly oriented towards adherence to this type of schemes, since its nature implies constant institutional learning based not only on its own experience but also on the exchange of knowledge in the academic environment. In Colombia, higher education institutions perceive an opportunity for improvement in the high quality standards of the National Accreditation Council, which requires an infrastructure oriented to the strategic knowledge management. This article presents the experience of implementing a prototype of a knowledge management system in a higher education undergraduate program. It describes the phases of the process and its relationship with the technological platform that supported the development. The conclusions highlight the advantages of the exercise, emphasizing institutional learning, the importance of the technological platform and, finally, reflections on knowledge management as a fundamental axis of accreditation processes.

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14

Patel-Schneider, Peter F. "Practical, object-based knowledge representation for knowledge-based systems." Information Systems 15, no. 1 (January 1990): 9–19. http://dx.doi.org/10.1016/0306-4379(90)90013-f.

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15

Johnson, N. E. "Knowledge based systems series Vol. 1: Knowledge acquisition for knowledge based systems Vol. 2: Knowledge acquisition tools for expert systems." Knowledge-Based Systems 3, no. 3 (September 1990): 181. http://dx.doi.org/10.1016/0950-7051(91)90030-6.

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16

Bouraga, Sarah, Ivan Jureta, Stéphane Faulkner, and Caroline Herssens. "Knowledge-Based Recommendation Systems." International Journal of Intelligent Information Technologies 10, no. 2 (April 2014): 1–19. http://dx.doi.org/10.4018/ijiit.2014040101.

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Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.

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17

Peppard, J., and P. L. Henry. "Corporate Knowledge‐Based Systems:." Management Decision 26, no. 6 (June 1988): 42–46. http://dx.doi.org/10.1108/eb001528.

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18

Yoon, Youngohc, and Tor Guimaraes. "Developing Knowledge-Based Systems." Information Resources Management Journal 5, no. 3 (July 1992): 16–32. http://dx.doi.org/10.4018/irmj.1992070102.

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19

Huws, Howell, Marcy Wintrub, and Nancy Martin. "KNOWLEDGE-BASED SYSTEMS DEVELOPMENT." Information Systems Management 9, no. 3 (January 1992): 51–56. http://dx.doi.org/10.1080/10580539208906882.

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20

Addis, T. R., and Mudigonda N. Sastry. "Designing Knowledge-Based Systems." IEEE Expert 1, no. 4 (November 1986): 91. http://dx.doi.org/10.1109/mex.1986.4307018.

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21

Ras, Zbigniew W. "Cooperative Knowledge-Based Systems." Intelligent Automation & Soft Computing 2, no. 2 (January 1996): 193–201. http://dx.doi.org/10.1080/10798587.1996.10750667.

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22

Shepherd, Anne. "Knowledge-based expert systems." Expert Systems with Applications 14, no. 4 (April 1998): 433–41. http://dx.doi.org/10.1016/s0957-4174(98)00003-7.

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23

Dawson, Kieran, John Cox, and Owen Smith. "Computer-based knowledge systems." Lancet 339, no. 8786 (January 1992): 185–86. http://dx.doi.org/10.1016/0140-6736(92)90256-3.

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24

Ford, B., S. J. Hague, and R. M. J. Iles. "Numerical knowledge-based systems." Mathematics and Computers in Simulation 31, no. 4-5 (October 1989): 395–400. http://dx.doi.org/10.1016/0378-4754(89)90133-x.

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25

Shadbolt, N., E. Motta, and A. Rouge. "Constructing knowledge-based systems." IEEE Software 10, no. 6 (November 1993): 34–38. http://dx.doi.org/10.1109/52.241964.

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26

Wyatt, J. "Computer-based knowledge systems." Lancet 338, no. 8780 (December 1991): 1431–36. http://dx.doi.org/10.1016/0140-6736(91)92731-g.

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27

Connah, D. M., and C. A. Fishbourne. "Intelligent knowledge-based systems." Journal of the Institution of Electronic and Radio Engineers 55, no. 6 (1985): 223. http://dx.doi.org/10.1049/jiere.1985.0072.

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28

Fischer, Gerhard, Andreas Lemke, and Thomas Schwab. "Knowledge-based help systems." ACM SIGCHI Bulletin 16, no. 4 (April 1985): 161–67. http://dx.doi.org/10.1145/1165385.317487.

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29

Kumar, B. "Knowledge-based design systems." Computing Systems in Engineering 2, no. 4 (January 1991): 425. http://dx.doi.org/10.1016/0956-0521(91)90009-t.

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30

Yen, Chao, Di Xu, Yi Jun Du, Wei Qing Fang, and Yi Lin Zhang. "Knowledge-based instrument systems." Engineering Applications of Artificial Intelligence 1, no. 4 (December 1988): 269–76. http://dx.doi.org/10.1016/0952-1976(88)90045-0.

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31

Cuzzocrea, Alfredo. "Advanced knowledge-based systems." Data & Knowledge Engineering 69, no. 7 (July 2010): 661–63. http://dx.doi.org/10.1016/j.datak.2010.02.001.

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32

Mockler, Robert J. "Using knowledge-based systems for teaching and performing knowledge-based systems development." Expert Systems with Applications 1, no. 2 (January 1990): 103–16. http://dx.doi.org/10.1016/0957-4174(90)90020-u.

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33

Kim, Jae-Kyeong. "Knowledge acquisition for knowledge based decision systems." Applied Artificial Intelligence 11, no. 2 (March 1997): 131–50. http://dx.doi.org/10.1080/088395197118280.

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34

Preston, S., C. Chapman, M. Pinfold, and G. Smith. "Knowledge acquisition for knowledge-based engineering systems." International Journal of Information Technology and Management 4, no. 1 (2005): 1. http://dx.doi.org/10.1504/ijitm.2005.006401.

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35

Buta, Paul, and Stephen Springer. "Communicating the knowledge in knowledge-based systems." Expert Systems with Applications 5, no. 3-4 (January 1992): 389–94. http://dx.doi.org/10.1016/0957-4174(92)90022-k.

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36

Mohamed A. Wahdan, Mohamed A. Wahdan, and Manar Hassan Mohamed A. Wahdan. "Automatic Assessment of Materiality: A Knowledge-based Approach." International Journal of Computer Auditing 1, no. 1 (December 2019): 064–91. http://dx.doi.org/10.53106/256299802019120101004.

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<p>This paper explores how can a knowledge–based system (KBS) be used in auditing especially when the task requires using professional judgments. It concerned with modeling, implementing and evaluating a KBS called Materiality EXpert (MEX). MEX can assess the level of planning materiality and performance materiality as professionals act. Knowledge used to build MEX is acquired from literature, international standards on auditing, and experienced auditors using questionnaire as well as unstructured and structured interviews. MEX was evaluated by 34 auditors from different audit firms in Egypt including international audit firms. The evaluation results acquired from experienced auditors in Egypt indicated that MEX successfully executes the task of assessing the level of planning materiality and performance materiality. Moreover, MEX is efficient, effective, and acceptable from auditors for assessing the level of planning materiality and performance materiality.</p> <p> </p>

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37

Schoen, E., R. G. Smith, and B. G. Buchanan. "Design of knowledge-based systems with a knowledge-based assistant." IEEE Transactions on Software Engineering 14, no. 12 (1988): 1771–91. http://dx.doi.org/10.1109/32.9063.

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38

Lanzola, G., S. Quaglini, and M. Stefanelli. "Knowledge-Acquisition Tools for Medical Knowledge-Based Systems." Methods of Information in Medicine 34, no. 01/02 (1995): 25–39. http://dx.doi.org/10.1055/s-0038-1634578.

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Abstract:Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

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39

SANTAMARINA, C., and G. SALVENDY. "Fuzzy sets based knowledge systems and knowledge elicitation." Behaviour & Information Technology 10, no. 1 (January 1991): 23–40. http://dx.doi.org/10.1080/01449299108924269.

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40

Wagner, Peter. "Techniques of representing knowledge in knowledge-based systems." Agricultural Systems 41, no. 1 (January 1993): 53–76. http://dx.doi.org/10.1016/0308-521x(93)90081-c.

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41

Benaroch, Michel. "Roles of design knowledge in knowledge-based systems." International Journal of Human-Computer Studies 44, no. 5 (May 1996): 689–721. http://dx.doi.org/10.1006/ijhc.1996.0029.

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42

Simonovic, Slobodan P. "Knowledge-based systems and operational hydrology." Canadian Journal of Civil Engineering 18, no. 1 (February 1, 1991): 1–11. http://dx.doi.org/10.1139/l91-001.

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Knowledge-based systems were brought to the attention of hydrologists almost a decade ago. The application of knowledge-based systems technology is natural and appropriate for the field of hydrology because it contains numerous procedures developed from theory, actual practice, and experience. The emphasis of the present paper is on demystifying knowledge-based systems of artificial intelligence. After a detailed review of the most important applications to the field of hydrology, the original concept for applying knowledge-based technology is presented. The discussion ends with the list of possible benefits from the application of knowledge-based technology. An expert system for the selection of a suitable method for flow measurement in open channels is used as a case study to illustrate the discussion in the paper. The system has been designed for potential use in Environment Canada. Key words: expert system, water resources, hydrology, flow measurements.

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43

Šuman, Sabrina, Alen Jakupović, and Francesca Gržinić Kuljanac. "Knowledge-Based Systems for Data Modelling." International Journal of Enterprise Information Systems 12, no. 2 (April 2016): 1–13. http://dx.doi.org/10.4018/ijeis.2016040101.

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Data modelling is a complex process that depends on the knowledge and experience of the designers who carry it out. The quality of created models has a significant impact on the quality of successive phases of information systems development. This paper, in short, reviews the data modelling process, the entity-relationship method (ERM) and actors in the data modelling process. Further, in more detail it presents systems, methods, and tools for the data modelling process and identifies problems that occur during the development phase of an information system. These problems also represent the authors' motivation for conducting research that aims to develop a knowledge-based system (KBS) in order to support the data modelling process by applying formal language theory (particularly translation) during the process of conceptual modelling. The paper describes the main identified characteristics of the authors' new KB system that are derived from the analysis of existing systems, methods, and tools for the data modelling process. This represents the focus of the research.

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44

Rubin, Stuart H. "Knowledge-Based Software Prototyping and Reuse." International Journal on Artificial Intelligence Tools 06, no. 01 (March 1997): 127–47. http://dx.doi.org/10.1142/s0218213097000074.

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Models are executable prototypes. Modeling is closely tied to simulation, which refers to the exercise of a model over a variable parametric space. Model simulations not only provide the engineer with feedback pertaining to the validity of a proposed design, but additionally allow competing designs to be compared on one or more parameters (i.e., sensitivity analysis). Models are defined from a base of several hundred primitive constructs. These constructs can define additional constructs hierarchically. An expertn–system was constructed, which retrieves software for reuse. This expert system is itself reusable and consists of many sub-systems – any one of which can invoke any other. A key feature is that any expertn–system need never be modified, for purposes of reuse, once saved in a repository. Rather, it communicates all information back to the caller and lets the caller decide how and when to use it. Thus, blocks in an expertn–system have very low coupling (i.e., no off-model connections). In addition, expertn–systems are, as their name suggests, organized in a hierarchy. This means that very complex decision-making systems can be called into play with minimal effort. Growing the repository is equivalent to learning.

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45

Song, Li Xia, and Yi Hui Zhang. "On Knowledge Based Intelligent Systems." Applied Mechanics and Materials 448-453 (October 2013): 3557–60. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3557.

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This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advanceof the technologies involved.

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46

Speller, M. A., Georgios I. Doukidis, Frank Land, and Gordon Miller. "Knowledge Based Management Support Systems." Journal of the Operational Research Society 41, no. 3 (March 1990): 264. http://dx.doi.org/10.2307/2583823.

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47

Hasan, H., and E. Gould. "Activity-based Knowledge Management Systems." Journal of Information & Knowledge Management 02, no. 02 (June 2003): 107–15. http://dx.doi.org/10.1142/s0219649203000048.

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This paper explores the design of computer-based systems that support the management of organisational knowledge. The information systems notions of databases and information architectures are extended to the search for a meaningful conceptual structure by which collective knowledge can be supported by existing information technologies. An "architecture" is presented where the key unit of analysis is human activity as proposed by the cultural–historical activity theory. This architecture has the richness of the activity theory philosophy and yet addresses the issues of practical implementation from both the technical and organisational perspectives. The paper articulates the architecture and also reports on a study where its effectiveness was evaluation by a target group. An implementation of the architecture is also introduced.

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48

Dologite, D. G., and Robert J. Mockler. "Developing Effective Knowledge-Based Systems." Information Resources Management Journal 2, no. 1 (January 1989): 27–40. http://dx.doi.org/10.4018/irmj.1989010103.

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49

Armstrong, Jack. "KNOWLEDGE-BASED SYSTEMS FOR USERS." Information Systems Management 9, no. 1 (January 1992): 28–37. http://dx.doi.org/10.1080/10580539208906846.

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50

Seviora, R. E. "Knowledge-Based Program Debugging Systems." IEEE Software 4, no. 3 (May 1987): 20–32. http://dx.doi.org/10.1109/ms.1987.230396.

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