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Journal articles on the topic 'Knowledge modelling'

To see the other types of publications on this topic, follow the link: Knowledge modelling.

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Mougin, Jonathan, Jean-Francois Boujut, Franck Pourroy, and Grégory Poussier. "Modelling knowledge transfer: A knowledge dynamics perspective." Concurrent Engineering 23, no. 4 (July 10, 2015): 308–19. http://dx.doi.org/10.1177/1063293x15592185.

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2

Nwana, Hyacinth S., Trevor J. M. Bench-Capon, Ray C. Paton, and Michael J. R. Shave. "Domain-driven knowledge modelling for knowledge acquisition." Knowledge Acquisition 6, no. 3 (September 1994): 243–70. http://dx.doi.org/10.1006/knac.1994.1013.

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3

Smedley, Jo. "Modelling personal knowledge management." OR Insight 22, no. 4 (October 26, 2009): 221–33. http://dx.doi.org/10.1057/ori.2009.11.

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4

Skačkauskienė, Ilona, Eglė Kazlauskienė, and Aušra Katinienė. "Modelling Knowledge Synergy Evaluation." Montenegrin Journal of Economics 13, no. 1 (March 2017): 35–47. http://dx.doi.org/10.14254/1800-5845/2017.13-1.14.

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5

Skackauskiene, Ilona, Egle Kazlauskiene, and Aushra Katiniene. "Modelling Knowledge Synergy Evaluation." Montenegrin Journal of Economics 13, no. 1 (March 2017): 35–49. http://dx.doi.org/10.14254/1800-5845/2017.13-1.2.

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6

Kovács, László, and Sándor Radeleczki. "Uncertainty Management in Knowledge Modelling." Procedia Technology 19 (2015): 4–11. http://dx.doi.org/10.1016/j.protcy.2015.02.002.

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7

Czejdo, Bogdan Denny. "Capturing dynamicity in knowledge modelling." International Journal of Knowledge and Learning 6, no. 2/3 (2010): 99. http://dx.doi.org/10.1504/ijkl.2010.034747.

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8

Krell, Moritz, and Dirk Krüger. "University students’ meta-modelling knowledge." Research in Science & Technological Education 35, no. 3 (January 20, 2017): 261–73. http://dx.doi.org/10.1080/02635143.2016.1274724.

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9

Chan, Christine W. "Knowledge acquisition by conceptual modelling." Applied Mathematics Letters 5, no. 3 (May 1992): 7–12. http://dx.doi.org/10.1016/0893-9659(92)90027-7.

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10

Stenning, K. "Modelling knowledge-based creative design." Knowledge-Based Systems 7, no. 4 (December 1994): 282. http://dx.doi.org/10.1016/0950-7051(94)90045-0.

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11

KEPPENS, J., and B. SCHAFER. "Knowledge based crime scenario modelling." Expert Systems with Applications 30, no. 2 (February 2006): 203–22. http://dx.doi.org/10.1016/j.eswa.2005.07.011.

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12

Wright, George, and Peter Ayton. "Eliciting and modelling expert knowledge." Decision Support Systems 3, no. 1 (March 1987): 13–26. http://dx.doi.org/10.1016/0167-9236(87)90032-7.

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13

Maria Teresa Guaglianone, and Nada Matta. "MNEMO (Methodology for Knowledge Acquisition and Modelling):Definition of a Global Knowledge Management Approach Combining Knowledge Modelling Techniques." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 12 (July 15, 2012): 160–69. http://dx.doi.org/10.4156/aiss.vol4.issue12.19.

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14

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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15

Arnold, Thorsten R. "Procedural knowledge for integrated modelling: Towards the Modelling Playground." Environmental Modelling & Software 39 (January 2013): 135–48. http://dx.doi.org/10.1016/j.envsoft.2012.04.015.

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16

Noennig, Jörg Rainer, Anna-Maria Scheler, Katarzyna Piskorek, and Jan Barski. "Towards Knowledge Ecosystems: Modelling Knowledge Dynamics in Environmental Systems." Procedia Computer Science 35 (2014): 1360–69. http://dx.doi.org/10.1016/j.procs.2014.08.179.

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17

Kim, Mik, and Wusheng Zhang. "Data Modelling Approaches to Knowledge Representation." International Journal of Technology, Knowledge, and Society 2, no. 3 (2006): 25–32. http://dx.doi.org/10.18848/1832-3669/cgp/v02i03/55614.

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18

Kessel, Thomas. "Information Modelling and Knowledge Bases IV." AI Communications 6, no. 3-4 (1993): 232. http://dx.doi.org/10.3233/aic-1993-63-411.

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19

USCHOLD, MIKE. "Knowledge level modelling: concepts and terminology." Knowledge Engineering Review 13, no. 1 (March 1998): 5–29. http://dx.doi.org/10.1017/s0269888998001040.

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We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.

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20

Paton, R. C. "Diagrammatic representations for modelling biological knowledge." Biosystems 66, no. 1-2 (June 2002): 43–53. http://dx.doi.org/10.1016/s0303-2647(02)00032-1.

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21

de Gelder, Johan. "Conceptual modelling of building regulation knowledge." Artificial Intelligence in Engineering 11, no. 3 (July 1997): 273–84. http://dx.doi.org/10.1016/s0954-1810(96)00043-x.

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22

Mellor, Robert B. "Big data modelling the knowledge economy." International Journal of Knowledge-Based Development 9, no. 3 (2018): 206. http://dx.doi.org/10.1504/ijkbd.2018.094896.

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23

Mellor, Robert B. "Big Data Modelling the Knowledge Economy." International Journal of Knowledge-Based Development 9, no. 3 (2018): 1. http://dx.doi.org/10.1504/ijkbd.2018.10012270.

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24

BLUNDELL, Tom, Devon CARNEY, Stephen GARDNER, Fiona HAYES, Brendan HOWLIN, Tim HUBBARD, John OVERINGTON, Diljeet Athwal SINGH, Bancinyane Lynn SIBANDA, and Michael SUTCLIFFE. "Knowledge-based protein modelling and design." European Journal of Biochemistry 172, no. 3 (March 1988): 513–20. http://dx.doi.org/10.1111/j.1432-1033.1988.tb13917.x.

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25

Sepúlveda, M., P. Levys, and M. Nussbaum. "Visiplan: A knowledge-based modelling tool." IEE Proceedings - Control Theory and Applications 143, no. 1 (January 1, 1996): 73–84. http://dx.doi.org/10.1049/ip-cta:19960088.

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26

Smołalski, Grzegorz. "Knowledge modelling in signal parameter evaluation." Measurement 42, no. 6 (July 2009): 863–70. http://dx.doi.org/10.1016/j.measurement.2009.01.009.

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27

Dubitzky, Wemer, Fionntina Carville, and John Hughes. "Case-level knowledge modelling in CBR." Irish Journal of Psychology 14, no. 3 (January 1993): 478–79. http://dx.doi.org/10.1080/03033910.1993.10557954.

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28

Kant, R., and M. D. Singh. "Knowledge management implementation: modelling the variables." International Journal of Innovation and Learning 6, no. 3 (2009): 342. http://dx.doi.org/10.1504/ijil.2009.023295.

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29

Menzies, Tim. "Applications of abduction: knowledge-level modelling." International Journal of Human-Computer Studies 45, no. 3 (September 1996): 305–35. http://dx.doi.org/10.1006/ijhc.1996.0054.

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30

Marincic, Jelena, Angelika Mader, Roel Wieringa, and Yan Lucas. "Reusing knowledge in embedded systems modelling." Expert Systems 30, no. 3 (August 30, 2012): 185–99. http://dx.doi.org/10.1111/j.1468-0394.2012.00631.x.

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31

Jr, Eugene Santos, Keum Joo Kim, Fei Yu, Deqing Li, and Joseph Rosen. "Bayesian knowledge modelling for healthcare practices." International Journal of Simulation and Process Modelling 8, no. 1 (2013): 52. http://dx.doi.org/10.1504/ijspm.2013.055207.

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32

Davies, Elizabeth. "Clinical role modelling: uncovering hidden knowledge." Journal of Advanced Nursing 18, no. 4 (April 1993): 627–36. http://dx.doi.org/10.1046/j.1365-2648.1993.18040627.x.

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33

Pennington, Deana D., Ioannis N. Athanasiadis, Shawn Bowers, Serguei Krivov, Joshua Madin, Mark Schildhauer, and Ferdinando Villa. "Indirectly driven knowledge modelling in ecology." International Journal of Metadata, Semantics and Ontologies 3, no. 3 (2008): 210. http://dx.doi.org/10.1504/ijmso.2008.023569.

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34

Hoede, C. "Modelling knowledge in Electronic Study Books." Journal of Computer Assisted Learning 10, no. 2 (January 1994): 104–12. http://dx.doi.org/10.1111/j.1365-2729.1994.tb00287.x.

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35

Curry, Bruce. "Knowledge‐based modelling for strategic decisions." Marketing Intelligence & Planning 14, no. 4 (July 1996): 24–28. http://dx.doi.org/10.1108/02634509610121532.

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36

Warner, Julian. "Modelling the diffusion of specialised knowledge." Aslib Proceedings 55, no. 1/2 (March 2003): 75–83. http://dx.doi.org/10.1108/00012530310462733.

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37

Gudas, Saulius. "ENTERPRISE KNOWLEDGE MODELLING: DOMAINS AND ASPECTS." Technological and Economic Development of Economy 15, no. 2 (June 30, 2009): 281–93. http://dx.doi.org/10.3846/1392-8619.2009.15.281-293.

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The paper presents the Knowledge‐based Enterprise framework aimed for the analysis of knowledge management and development of an Enterprise knowledge base. The approaches concerning the modelling of enterprise domains and aspects are presented and used for the development of the concept of Enterprise Knowledge Component. The Enterprise knowledge component (B, T, K) is defined as a composition of 3 obligatory parts: knowledge management methods (K), the knowledge about IT (T) services and tools, and business management knowledge (B). The formal modelling structure – the Enterprise Knowledge Space (B, T, K) is constructed for the refinement of the hierarchy of enterprise knowledge items. The Knowledge‐based Enterprise framework is represented as modified Value Chain Model including the knowledge management layer and IT component. The major knowledge subsets of the enterprise knowledge base are identified. Santrauka Pateiktas apibendrintas žiniomis grįstos veiklos modelis, skirtas žinių valdymui analizuoti ir kurti organizacijos (veiklos) žinių bazę. Aptarti veiklos domenų ir aspektų modeliavimo būdai, remiantis šia analize suformuluotas veiklos žinių komponento konceptas. Veiklos žinių komponentas (B, T, K) yra apibrėžtas kaip būtina trijų dalių visuma: žinių valdymo metodai (K), žinios apie IT paslaugas ir priemones (T) bei veiklos valdymo žinios (B). Sudaryta formali modeliavimo konstrukcija – organizacijos (veiklos) žinių erdvė (B, T, K), kuri atskleidžia veiklos žinių elementus. Apibendrintas žiniomis grįstos veiklos modelis pavaizduotas remiantis modifikuotos vertės grandinės modeliu, papildytu žinių valdymo sluoksniu ir IT komponentu. Identifikuoti pagrindiniai veiklos žinių bazės žinių poaibiai.

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38

Kamsu-Foguem, B., P. F. Tiako, E. Mutafungwa, and C. Foguem. "Knowledge-based modelling applied to synucleinopathies." European Geriatric Medicine 6, no. 4 (July 2015): 381–88. http://dx.doi.org/10.1016/j.eurger.2015.02.011.

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39

Barber, M., and S. Jackson. "‘Knowledge Making’: Issues in Modelling Local and Indigenous Ecological Knowledge." Human Ecology 43, no. 1 (February 2015): 119–30. http://dx.doi.org/10.1007/s10745-015-9726-4.

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40

Griffiths, David A., Serge Koukpaki, and Brian Yan Martin. "The Knowledge CORE." International Journal of Knowledge and Systems Science 1, no. 2 (April 2010): 1–14. http://dx.doi.org/10.4018/jkss.2010040101.

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This paper derives from a research project, which sets out to address practitioner dissatisfaction in the area of Knowledge Management. The author discusses common weaknesses in existing thinking about Knowledge Management and in prevailing models in particular. Modelling processes are considered and underlying assumptions that are required to be addressed in any attempt to create a Knowledge Management Model are examined. An overview is provided of the initial stages in the development of a new, synthetic and general model, The Knowledge Core©, with assumptions underlying this signposted and their influences upon modelling are discussed.

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41

Li, Jun, Xiaoying Zhang, Bin Lu, Raheel Ahmed, and Qian Zhang. "Static Geological Modelling with Knowledge Driven Methodology." Energies 12, no. 19 (October 8, 2019): 3802. http://dx.doi.org/10.3390/en12193802.

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Geological modelling is an important topic of oil and gas exploration and production. A new knowledge driven methodology of geological modelling is proposed to address the problem of “hard data” limitation and modelling efficiency of the conventional data driven methodology. Accordingly, a new geological modelling software (DMatlas) (V1.0, Dimue, Wuhan, China) has been developed adopting a grid-free, object-based methodology. Conceptual facies models can be created for various depositional environments (such as fluvial, delta and carbonates). The models can be built largely based on geologists’ understandings with “soft data” such as outcrops analysis and geological maps from public literatures. Basic structures (fault, folds, and discrete fracture network) can be easily constructed according to their main features. In this methodology, models can be shared and re-used by other modelers or projects. Large number of model templates help to improve the modelling work efficiency. To demonstrate the tool, two case studies of geological modelling with knowledge driven methodology are introduced: (1) Suizhong 36-1 field which is a delta depositional environment in Bohai basin, China; (2) a site of the north Oman fracture system. The case studies show the efficiency and reliability within the new methodology.

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42

Girard, N., and B. Hubert. "Modelling expert knowledge with knowledge-based systems to design decision aids." Agricultural Systems 59, no. 2 (February 1999): 123–44. http://dx.doi.org/10.1016/s0308-521x(98)00085-7.

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43

Henderi, Henderi, Ary Budi Warsito, Ahmad Sidik, and Maimunah Maimunah. "DIGITAL LIBRARY MODELLING: SUPPORTING FOR KNOWLEDGE MANAGEMENT." CCIT Journal 3, no. 3 (May 3, 2010): 300–321. http://dx.doi.org/10.33050/ccit.v3i3.668.

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Peningkatan kebutuhan masyarakat terhadap informasi yang semakin tinggi membuat eksistensi perpustakaan semakin penting. Selain merupakan sumber informasi dan referensi berbagai jenis ilmu pengetahuan, perpustakaan juga memiliki peran strategis pengembangan ilmu pengetahuan. Pada sisi lain, hasil penelitian menunjukan bahwa sebagian perpustakaan belum memar faatkan information technology (17) secara optimal dalam pengolahan pengetahuan. Akibatnya informasi dan referensi yang dimiliki perpustakaan tidak mudah diperoleh oleh publik untuk dikembangkan. Untuk itu, artikel ini membahas digital library modelling untuk mendukung penerapan model knowledge management, dan mempermudah perolehan ir formasi dan pengembangan ilmu pengetahuan dari perpustakaan. Digital library modelling dibuat menggunakan pendekatan unifield modelling language (UML), dan digambarkan dalam bentuk: use case diagram, class diagram, state diagram, collaboration diagram, component diagram, dan screen prototype. Hasil implementasi digital library juga dibahas secara ringkas untuk memberikan gambaran lebih jelas.

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44

Hoppenbrouwers, Stijn, Bart Schotten, and Peter Lucas. "Towards Games for Knowledge Acquisition and Modeling." International Journal of Gaming and Computer-Mediated Simulations 2, no. 4 (October 2010): 48–66. http://dx.doi.org/10.4018/jgcms.2010100104.

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Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

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45

Čapkovič, František. "D.E.D.S. Modelling and Knowledge-Based Control 1." IFAC Proceedings Volumes 30, no. 21 (September 1997): 297–302. http://dx.doi.org/10.1016/s1474-6670(17)41454-6.

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46

Lothenbach, B., D. Damidot, T. Matschei, and J. Marchand. "Thermodynamic modelling: state of knowledge and challenges." Advances in Cement Research 22, no. 4 (October 2010): 211–23. http://dx.doi.org/10.1680/adcr.2010.22.4.211.

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47

Koponen, Ismo T., and Maija Nousiainen. "Modelling students’ knowledge organisation: Genealogical conceptual networks." Physica A: Statistical Mechanics and its Applications 495 (April 2018): 405–17. http://dx.doi.org/10.1016/j.physa.2017.12.105.

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48

Kudryavtsev, D., T. Gavrilova, M. Smirnova, and K. Golovacheva. "Modelling Consumer Knowledge: the Role of Ontology." Procedia Computer Science 176 (2020): 500–507. http://dx.doi.org/10.1016/j.procs.2020.08.052.

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49

Boswell, R., and S. Craw. "Knowledge modelling for a generic refinement framework." Knowledge-Based Systems 12, no. 5-6 (October 1999): 317–25. http://dx.doi.org/10.1016/s0950-7051(99)00018-0.

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50

Mira, J., and A. P. Del Pobil. "Editorial: Knowledge modelling for software component reuse." IEE Proceedings - Software 147, no. 5 (2000): 149. http://dx.doi.org/10.1049/ip-sen:20000901.

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