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Journal articles on the topic 'Hierarchy of knowledge'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Caria, Telmo H. "Knowledge hierarchy and professional knowledge." Cadernos de Pesquisa 44, no. 154 (2014): 798–826. http://dx.doi.org/10.1590/198053141992.

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The aim of this article is to substantiate, in the sociological point of view, the distinction between the social and cognitive processes that produce knowledge in knowledge abstract systems - KAS - to generate cultural inequality and the micro processes of knowledge usage, which build local and cultural knowledge from common sense. It is circumscribed to this aim a problematization of knowledge usage developed by middle class salaried professional groups, rich in cultural capital but without equivalent symbolical capital, in a capitalist society at risk. In order to achieve this goal, the cla

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2

Chen, ZiTan. "Hierarchy of knowledge in GIS." Science in China Series E: Technological Sciences 51, S1 (2008): 6–12. http://dx.doi.org/10.1007/s11431-008-5016-3.

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Frické, Martin. "The Knowledge Pyramid: the DIKW Hierarchy." KNOWLEDGE ORGANIZATION 46, no. 1 (2019): 33–46. http://dx.doi.org/10.5771/0943-7444-2019-1-33.

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4

Kim, Sang-Heon, and Na-Yun Kim. "Knowledge Hierarchy for Culture Contents Development." Journal of the Korea Contents Association 11, no. 12 (2011): 711–22. http://dx.doi.org/10.5392/jkca.2011.11.12.711.

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5

Perry, Deborah L. "Measuring Learning with the Knowledge Hierarchy." Visitor Studies 6, no. 1 (1993): 73–77. http://dx.doi.org/10.1080/10645579309444681.

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6

Hicks, Richard C., Ronald Dattero, and Stuart D. Galup. "The five‐tier knowledge management hierarchy." Journal of Knowledge Management 10, no. 1 (2006): 19–31. http://dx.doi.org/10.1108/13673270610650076.

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7

Morizet-Mahoudeaux, P. "A hierarchy of network-based knowledge systems." IEEE Transactions on Systems, Man, and Cybernetics 21, no. 5 (1991): 1184–91. http://dx.doi.org/10.1109/21.120068.

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8

Acar, William, Susan V. Iverson, and Rami S. Al-Gharaibeh. "Reconceptualizing the Knowledge Hierarchy for Management Education." International Journal of Knowledge Management 11, no. 1 (2015): 84–100. http://dx.doi.org/10.4018/ijkm.2015010105.

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Education is undergoing a crisis. Among the several remedial approaches proposed, this article contributes to the promotion of structured teaching for constructed knowing. One of the neglected areas of concern in the current education revolution is the structure of knowledge itself. The Data-Information-Knowledge (DIK) hierarchy was originally suggested to explain the differences between the three levels of knowledge; however, it does not capture them very well. Aiming at re-conceptualizing the DIK hierarchy from an up-to- date knowledge acquisition and management perspective, the authors star

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9

Heck, Paul. "THE HIERARCHY OF KNOWLEDGE IN ISLAMIC CIVILIZATION." Arabica 49, no. 1 (2002): 27–54. http://dx.doi.org/10.1163/15700580252933983.

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10

Byng, Michelle D. "RACE KNOWLEDGE." Du Bois Review: Social Science Research on Race 14, no. 1 (2017): 273–93. http://dx.doi.org/10.1017/s1742058x17000042.

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AbstractThis analysis addresses race knowledge or the connection between race identity and the ability to designate what is socially legitimate. It problematizes race inequality in light of neoliberal, post-Civil Rights racial reforms. Using qualitative data from interviews with second-generation Muslim Americans, the analysis maps their understanding of the racialized social legitimacy of Brown, Black, and White identities. Findings address how racial hierarchy is organized by racial neoliberalism and the persistence of White supremacy. They show that White racial dominance continues in spite

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11

Sharma, B. P., and M. D. Singh. "Modeling the Knowledge Sharing Barriers." International Journal of Knowledge-Based Organizations 5, no. 1 (2015): 16–33. http://dx.doi.org/10.4018/ijkbo.2015010102.

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In the knowledge-based economy, the survival and growth of the engineering industries depends upon the knowledge management (KM). In the present environment, KS is the corner stone of the KM. Some variables hinder KS in the industries are known as knowledge sharing barriers (KSBs). The objective of this paper is to identify and recognize the critical KSBs and their mutual influences in the industries. The interpretive structural modeling (ISM) methodology has been used to develop hierarchy of the identified KSBs evolving their mutual relationships. Identified KSBs at the root of the hierarchy

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12

Lara, Luis Fernando. ""Concepts" and Term Hierarchy." Terminology 5, no. 1 (1998): 59–76. http://dx.doi.org/10.1075/term.5.1.06lar.

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The paper proposes a coherent, exhaustive and verifiable theory of terminology. It demonstrates the empirical and methodological inadequacy of the notion of "concept" in philosophical semantics and Wusterian terminology. It maintains that the social nature of cognition allocates linguistics an important role in the construction and validation of human knowledge. It suggests a four stage hierarchy for classifying lexical units based upon four successive stages of knowledge acquisition.

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13

Abbasi, Saliha Gul, and Ghulam Dastgeer. "Organizational Culture and Knowledge Sharing Behavior: Examining Serial Mechanisms." Sukkur IBA Journal of Management and Business 5, no. 1 (2018): 33. http://dx.doi.org/10.30537/sijmb.v5i1.114.

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This study aims to examine how hierarchy organizational culture affect the knowledge sharing behavior of teachers in higher education institutions and universities and its mediated link through formal knowledge governance mechanism and knowledge sharing opportunity. A sample of 269 teachers was drawn from university teachers in Pakistan and structural equation modeling is used to test the hypotheses. The results of this study suggest that first, hierarchy organizational culture is positively associated with knowledge sharing behavior of teachers. Second, formal knowledge governance mechanism f

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Yerbury, Hilary. "Re-valuing Women's Knowledge." Cosmopolitan Civil Societies: An Interdisciplinary Journal 5, no. 3 (2013): 1–13. http://dx.doi.org/10.5130/ccs.v5i3.3381.

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Women’s knowledge has often been seen as “a whole set of knowledges that have been disqualified as inadequate to their task or insufficiently elaborated: naive knowledges, located low down on the hierarchy, beneath the required level of cognition or scientificity." (Foucault 1980, p. 82). In this description, scientific knowledges are seen to be hierarchically more important, with traditional knowledges ranged beneath them. In this hierarchy, women’s knowledges are found wanting. The purpose of this paper is to explore the assertion that women’s knowledges are inadequate and to document ways i

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15

Yang, Xibei, Xiaoning Song, Yanhong She, and Jingyu Yang. "Hierarchy on multigranulation structures: a knowledge distance approach." International Journal of General Systems 42, no. 7 (2013): 754–73. http://dx.doi.org/10.1080/03081079.2013.810625.

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16

김홍주. "National-Space Hierarchy Structure of Creative Knowledge Production." Korea Spatial Planning Review 53, no. ll (2007): 3–20. http://dx.doi.org/10.15793/kspr.2007.53..001.

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17

Stanley, T. D. "Recursive economic knowledge: Hierarchy, maximization and behavioral economics." Journal of Behavioral Economics 15, no. 4 (1986): 85–99. http://dx.doi.org/10.1016/0090-5720(86)90041-0.

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18

Zhang, Zhanqiu, Jianyu Cai, Yongdong Zhang, and Jie Wang. "Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 3065–72. http://dx.doi.org/10.1609/aaai.v34i03.5701.

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Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model—namely, Hierarchy-Aware Knowledge Graph

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19

Bernstein, Jay H. "The Data-Information-Knowledge-Wisdom Hierarchy and its Antithesis." NASKO 2, no. 1 (2011): 68. http://dx.doi.org/10.7152/nasko.v2i1.12806.

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20

Labarrière, Jean-Louis, Janine Alexandra Treves, and Jennifer Curtiss Gage. "The Articulation and Hierarchy of Knowledge in Aristotle's Thought." Diogenes 45, no. 178 (1997): 23–31. http://dx.doi.org/10.1177/039219219704517803.

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21

Xiao, Liang, and Des Greer. "A knowledge hierarchy model for adaptive multi-agent systems." International Journal of Computer Applications in Technology 31, no. 1/2 (2008): 3. http://dx.doi.org/10.1504/ijcat.2008.017715.

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22

Allatta, Joan T., and Elizabeth Ann Reusch. "Intra-organizational Knowledge Transfer: How language and hierarchy matter." Academy of Management Proceedings 2012, no. 1 (2012): 12091. http://dx.doi.org/10.5465/ambpp.2012.12091abstract.

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23

Frické, Martin. "The knowledge pyramid: a critique of the DIKW hierarchy." Journal of Information Science 35, no. 2 (2008): 131–42. http://dx.doi.org/10.1177/0165551508094050.

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24

Ding, Liya. "A Model of Hierarchical Knowledge Representation – Toward Knowware for Intelligent Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 10 (2007): 1232–40. http://dx.doi.org/10.20965/jaciii.2007.p1232.

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We propose a model for multiresolutionary knowledge representation; define concepts of domain, application, and working hierarchies; and discuss inference mechanisms in the knowledge hierarchy. We also introduce an automatic construction of the knowledge hierarchy for the development of intelligent systems.

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25

Azoulay, Pierre. "Capturing Knowledge within and across Firm Boundaries: Evidence from Clinical Development." American Economic Review 94, no. 5 (2004): 1591–612. http://dx.doi.org/10.1257/0002828043052259.

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How do firm boundaries influence employees' acquisition of information? Using detailed project-level data and qualitative evidence, I document that pharmaceutical firms are more likely to outsource the coordination of data-intensive clinical trials, while they are more likely to assign knowledge-intensive trials to internal teams. Managers do not choose between market and hierarchy, but between the hierarchy of the firm—in which subjective performance evaluations are combined with flat explicit incentives—and the hierarchy of its subcontractor—whose virtue stems precisely from the ability to p

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26

Montare, Alberto. "Knowledge Acquired from Learning: Procedural Cognition and its Declarative Cognizance." Perceptual and Motor Skills 74, no. 1 (1992): 243–57. http://dx.doi.org/10.2466/pms.1992.74.1.579.

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62 college students articulated the procedural cognition acquired during successful learning of both original and reversal-shifts of the discrimination-reversal learning task. Articulations formed a four-level hierarchy of “declarative cognizance” (defined as correct articulation of reinforcement contingencies) as follows: Level 1 having no declarative cognizance, Level 2 of perceptually based cognizance, Level 3 of concrete-rule-based cognizance, and Level 4 of abstract-rule-based cognizance. The plausibility of this cognitive hierarchy is enhanced by observations that increasingly higher lev

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27

Craig, Denise Patricia, and Fintan Thompson. "Clinicians’ consent law knowledge: The case for education." Focus on Health Professional Education: A Multi-Professional Journal 21, no. 3 (2020): 65–77. http://dx.doi.org/10.11157/fohpe.v21i3.394.

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Introduction: The short-term objectives of this study were to investigate clinicians’ confidence with, and knowledge of, consent laws, their behaviour regarding familiarisation with patients’ advance care plans and the potential benefit of brief education. Education covered patients’ rights to refuse treatment, including via advance directive, and the legislated hierarchy of decision-making authority.Methods: Throughout 2018, all clinicians at one Queensland Hospital and Health Service were invited to attend a 1-hour legal education session. The study used a crosssectional survey to measure cl

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28

Tuomi, Ilkka. "Data Is More than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory." Journal of Management Information Systems 16, no. 3 (1999): 103–17. http://dx.doi.org/10.1080/07421222.1999.11518258.

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29

Kant, R., and M. D. Singh. "Knowledge Management Implementation: Modeling the Barriers." Journal of Information & Knowledge Management 07, no. 04 (2008): 291–305. http://dx.doi.org/10.1142/s021964920800210x.

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The purpose of this paper is to present an approach to successful KM implementation by understanding the dynamics among various knowledge management barriers (KMBs) that hinder the implement of KM in organisations. Using interpretive structural modeling (ISM), the research presents a hierarchy-based model and the mutual relationship among the KMBs. The research shows that there is a group of KMBs having high driving power and low dependence which require maximum attention and are of strategic importance. Another group consists of those KMBs which have high dependence and low driving power whic

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30

Hawthorne, F. C. "The structure hierarchy hypothesis." Mineralogical Magazine 78, no. 4 (2014): 957–1027. http://dx.doi.org/10.1180/minmag.2014.078.4.13.

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AbstractThe structure hierarchy hypothesis states that structures may be ordered hierarchically according to the polymerization of coordination polyhedra of higher bond valence. A mathematical hierarchy is an ordered set of elements where the ordering reflects a natural hierarchical relation between (or arrangement of) the elements. Here, I review the structure hierarchies for the borate, uranyl oxide, phosphate, sulfate, beryllate and oxide-centred Cu, Pb and Hg minerals (plus synthetics where appropriate). Structure hierarchies have two functions: (1) they serve to organize our knowledge of

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31

Lee, Eun-Hye, and Yeonsuk Bae. "A Study on Hierarchy among Sub-Characteristics of Vocabulary Knowledge." Korean Association For Learner-Centered Curriculum And Instruction 20, no. 8 (2020): 303–23. http://dx.doi.org/10.22251/jlcci.2020.20.8.303.

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32

Lamb, Stephen, and Ariel Araos. "Curriculum Hierarchy and Devalued Knowledge: The Case of Environmental Education." Curriculum and Teaching 11, no. 2 (1996): 19–32. http://dx.doi.org/10.7459/ct/11.2.03.

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33

Baines, John. "Restricted Knowledge, Hierarchy, and Decorum: Modern Perceptions and Ancient Institutions." Journal of the American Research Center in Egypt 27 (1990): 1. http://dx.doi.org/10.2307/40000070.

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34

Santoso, Heru Agus, Su-Cheng Haw, and Ziyad T. Abdul-Mehdi. "Ontology extraction from relational database: Concept hierarchy as background knowledge." Knowledge-Based Systems 24, no. 3 (2011): 457–64. http://dx.doi.org/10.1016/j.knosys.2010.11.003.

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35

Zeng, Qingtian. "Auto-construction for Knowledge Inheritance Hierarchy of Concepts within NKIMath." Information Technology Journal 10, no. 2 (2011): 348–57. http://dx.doi.org/10.3923/itj.2011.348.357.

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36

Agrela Romero, Belén, and Amalia Morales Villena. "Knowledge Hierarchy of Social Work and Gender Studies in Spain." Affilia 32, no. 3 (2017): 276–91. http://dx.doi.org/10.1177/0886109917692373.

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This article explores the gendered nature of social work and some of the consequences this has in academia, research, and professional practice in Spain. The authors examine the connections between social work and gender studies in academia in Spain, reflecting on the position these disciplines occupy in the current hierarchy of knowledge and the knowledge production system. The impact of the university reforms under the European Union’s (EU) Bologna plan is analyzed in the context of the commercialization of knowledge. The obstacles that prevent the value of these disciplines from being recog

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37

You, Noritomi, Yasuhiko Kato, and Masatoshi Kitaoka. "Numerous date in hierarchy for knowledge concentrated in decision tree." Computers & Industrial Engineering 27, no. 1-4 (1994): 535–38. http://dx.doi.org/10.1016/0360-8352(94)90352-2.

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38

Nayak, Guruprasad, Sourav Dutta, Deepak Ajwani, Patrick Nicholson, and Alessandra Sala. "Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs." Information Retrieval Journal 22, no. 3-4 (2018): 256–84. http://dx.doi.org/10.1007/s10791-018-9345-y.

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39

Huxue, Ruobai, Jie Huang, Jiantao Wang, and Yiming Li. "Learning Gaussian Hierarchy Embedding for Relation Prediction in Knowledge Graph." Journal of Physics: Conference Series 1748 (January 2021): 032039. http://dx.doi.org/10.1088/1742-6596/1748/3/032039.

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40

Ben-Eliyahu, R. "A Hierarchy of Tractable Subsets for Computing Stable Models." Journal of Artificial Intelligence Research 5 (August 1, 1996): 27–52. http://dx.doi.org/10.1613/jair.223.

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Finding the stable models of a knowledge base is a significant computational problem in artificial intelligence. This task is at the computational heart of truth maintenance systems, autoepistemic logic, and default logic. Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes of knowledge bases, Omega_1,Omega_2,..., with the following properties: first, Omega_1 is the class of all stratified knowledge bases; second, if a knowledge base Pi is in Omega_k, then Pi has at most k stable models, and all of them may be found in time O(lnk), where l is the length of the knowled

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41

Patil, Nitin Yashwant, and Ravi M. Warkhedkar. "Knowledge management implementation in Indian automobile ancillary industries." Journal of Modelling in Management 11, no. 3 (2016): 802–10. http://dx.doi.org/10.1108/jm2-04-2015-0018.

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Purpose In the past decade, much has been written about knowledge management (KM) in the manufacturing; however, less attention has been paid to the Indian automobile ancillary industries located in Chinchwad, Pune. It is suitable to find out the relationship of the factors of the study. It helps in identifying the hierarchy of factors to be taken, and interlinking of production department with KM improves the productivity of the industries. Categorization of these principles based on their driving power (principles which hold other principles) and dependence (principles which are dependent on

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42

Li, Cheng-Te, and Hong-Yu Lin. "Structural Hierarchy-Enhanced Network Representation Learning." Applied Sciences 10, no. 20 (2020): 7214. http://dx.doi.org/10.3390/app10207214.

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Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detectio

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43

Wibawa, I. Nyoman Gautama Satria. "Hierarchy and Characteristic of Storage Devices." International Research Journal of Management, IT & Social Sciences 2, no. 3 (2015): 1. http://dx.doi.org/10.21744/irjmis.v2i3.57.

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This paper explores information about hierarchy and characteristic of storage devices. Lack of knowledge about storage device that used by everyone in the world is the main background of this paper. People can understand what the different of types, function, advantage and disadvantage of several storage devices. This paper examines several articles from online source, and writer’s experience in use of storage devices. In order to gain a complete understanding of Storage Device’s types, divisions, functions and relationships, it is necessary to conduct a study that examines all parts of storag

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44

TAN, AH-HWEE, and HUI-SHIN VIVIEN SOON. "CONCEPT HIERARCHY MEMORY MODEL: A NEURAL ARCHITECTURE FOR CONCEPTUAL KNOWLEDGE REPRESENTATION, LEARNING, AND COMMONSENSE REASONING." International Journal of Neural Systems 07, no. 03 (1996): 305–19. http://dx.doi.org/10.1142/s0129065796000270.

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This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a c

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45

HOLDEN, ALISTAIR D. C., and STEVEN C. SUDDARTH. "COMBINED NEURAL-NET/KNOWLEDGE-BASED ADAPTIVE SYSTEMS FOR LARGE SCALE DYNAMIC CONTROL." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 04 (1991): 503–22. http://dx.doi.org/10.1142/s0218001491000296.

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The control of small-scale systems using either knowledge-based or neural net methods is quite feasible. Large scale systems, however, introduce complexities in modeling and excessive computation time. This paper attacks these difficulties by breaking down the problem into a hierarchy of control contexts. The lowest level of this hierarchy is implemented as rule sets and/or neural networks. A method using "hints" is shown to greatly reduce training time in back-propagation neural nets.

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46

Pop, Ioan G., Mihai-Florin Talpos, and Igor Prisac. "A Transdisciplinary Approach on the Advanced Sustainable Knowledge Integration." Balkan Region Conference on Engineering and Business Education 1, no. 1 (2015): 215–25. http://dx.doi.org/10.1515/cplbu-2015-0025.

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AbstractThe paper presents a new, transdisciplinary approach on the DIKW (Data, Information, Knowledge, and Wisdom) hierarchy, offering arguments that the hierarchy is unsound and even methodologically undesirable. The purpose of the paper is to identify a new and more complete perspective on knowledge integration. This model is based on another scale, in a synergistic-generative transdisciplinary manner, in order to transfer and implement knowledge in the knowledge based society/economy context. The new knowledge pattern, named DIMLAK (Data, Information, Messages, Learning, and Advanced Knowl

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47

Pandey, Anoop Kumar. "Concept Identification Using Co-Occurrence Graph." International Journal of Web Portals 10, no. 1 (2018): 27–38. http://dx.doi.org/10.4018/ijwp.2018010103.

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In a community setting, Utilitarian Knowledge or “Knowledge that works” are routinely diffused through social media interactions. The aggregation of this knowledge is a divergent process, where common knowledge gets segregated into several local worlds of utilitarian knowledge. To capture and represent this knowledge, several data models have been proposed. One of the model organizes concepts (atomic elements) in a hierarchy namely concept hierarchy (“is-a”) in which concepts are added manually at the most appropriate level inside the hierarchy. To minimize manual intervention in entity resolu

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48

Albert, Lillie, Rina Kim, and Na Kwon. "A Hierarchy of South Korean Elementary Teachers’ Knowledge for Teaching Mathematics." Education Practice and Innovation 1, no. 2 (2014): 51–73. http://dx.doi.org/10.15764/epi.2014.02007.

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49

Hetsroni, Amir. "GLOBALIZATION AND KNOWLEDGE HIERARCHY THROUGH THE EYES OF A QUIZ SHOW." Innovation: The European Journal of Social Science Research 18, no. 4 (2005): 385–405. http://dx.doi.org/10.1080/13511610500384145.

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50

Puthpongsiriporn, T., J. D. Porter, B. Bidanda, M. E. Wang, and R. E. Billo. "Attribute-level neighbor hierarchy construction using evolved pattern-based knowledge induction." IEEE Transactions on Knowledge and Data Engineering 18, no. 7 (2006): 917–29. http://dx.doi.org/10.1109/tkde.2006.104.

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