• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.209-213
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• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.169-174
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• 2022

The study aims to discover the scope of pre-service special education teachers' knowledge and perceptions of using computer technology in teaching students with disabilities from a pre-service teacher (PST) perspective in light of the gender and sub-major variables. The sample consisted of 84 MEd students/pre-service teachers at the Department of Special Education, Faculty of Education, Umm Al-Qura University. The descriptive analytical approach is used due to its relevance to the study. A survey consisting of the participant's basic information section and 12 statements was sent to a set of pre-service teachers. Findings showed that pre-service special education teachers had an overall high knowledge of using computer technology (M=3.93). Findings also indicated that there were no gender- or major-related statistically significant differences (α = 0.05), in pre-service special education students' knowledge and perceptions of using computer technology.

• Knowledge Management Research
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• v.22 no.4
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• pp.1-26
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• 2021

Measuring the intangible asset has been vigorously studied for its importance. Especially, the value of character in media industry is difficult to quantitatively evaluate in spite of the industry's rapid growth. Recently, the Social Network Analysis (i.e., SNA) has been actively applied to understand human usage patterns in a media field. By using SNA methodology, this study attempts to investigate how the character network characteristics of media works are linked to human search behaviors. Our analysis reveals the positive correlation and causality between character network centralities and character search data. This result implies that the character network can be used as a clue for the valuation of character assets.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.1
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• pp.217-231
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• 2015

This study aims to infer the gene-protein 'brings_about' chains of pancreatic cancer which were referred to in the pancreatic cancer related researches by constructing the gene-protein interaction network of pancreatic cancer. The chains can help us uncover publicly unknown knowledge that would develop as empirical studies for investigating the cause of pancreatic cancer. In this study, we applied a novel approach that grafts text mining and the main path analysis into Swanson's ABC model for expanding intermediate concepts to multi-levels and extracting the most significant path. We carried out text mining analysis on the full texts of the pancreatic cancer research papers published during the last ten-year period and extracted the gene-protein entities and relations. The 'brings_about' network was established with bio relations represented by bio verbs. We also applied main path analysis to the network. We found the main direct 'brings_about' path of pancreatic cancer which includes 14 nodes and 13 arcs. 9 arcs were confirmed as the actual relations emerged on the related researches while the other 4 arcs were arisen in the network transformation process for main path analysis. We believe that our approach to combining text mining analysis with main path analysis can be a useful tool for inferring undiscovered knowledge in the situation where either a starting or an ending point is unknown.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.691-699
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• 2011

Recently, diverse approaches retrieval services based on social network are suggested. Although existing recommendation systems can retrieve experts of specific fields, profiles and evaluations about experts that users want to be recommended are in a system. The proposed expert recommendation system can automatize collection of evaluation to evaluate experts and experts' profiles in separate systems by using the Knowledge Base and XMDR. We also attempt to construct system which can recommend a number of experts by dynamically constructing Social Network by using diverse resources distributed 로컬ly and composed of heterogeneous data sources. To resolve these problems efficiently, there is a need to provide constructed resources between heterogeneous systems with transparency and independence and provide users with a singular interface. Therefore, the proposed system in this paper uses Knowledge Base and XMDR for extracting distributed experts' profiles and designs expert recommendation system connecting Knowledge Base with Social Network.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.191-210
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• 2022

Niklas Luhmann is a sociologist who has had a strong influence on other disciplines. Therefore, it is necessary to examine what Luhmann's theory has influenced on his subsequent researchers. This study analyzed the knowledge network of studies on Niklas Luhmann's theory by follow-up researchers. Bibliographic coupling and co-citation were used as the analysis method of knowledge network. The main results are as follows. First, the language clusters were divided into Latin American / Spanish-speaking regions, Western Europe / Anglo-American regions, Eastern / Northern Europe and other language regions through bibliographic coupling analysis. Second, from the node analysis of bibliographic coupling, It was divided into 2 main cases: where Luhmann's major works were cited; where Luhmann's minor works were cited. Third, it was found that there are the core work groups that are repeatedly cited among Luhmann's works. Fourth, 12 core works were derived from the node analysis of the co-citation network, and appeared in four groups according to themes.

• Journal of Korea Technology Innovation Society
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• v.20 no.2
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• pp.487-517
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• 2017

In the context of an increased demand in universities' expansion of networks between other innovation actors, this research attempts to make a comparison on university-centered SCIE knowledge networks between regions. Using regional comparison, we have looked into these networks in regards to their characteristics, the importance of regional boundaries, and the effect of the regional industrial policy. As a result of this comparative analysis, we discovered that the point universities and research universities hold high centrality in regional knowledge networks, and that the characteristics of regions are reflected into this network. For instance, the Gyeonggi province had a preeminent level of industry-academy relationship, while for Daejeon it was public research institutions and academy, and Gangwon province it was between academy between academy. As a network analysis based on journals above SCIE levels, regional boundaries were not very clear in the network structures. However, within these boundaries, the impact of regional industrial policies were proven to be stronger in the Gang-won province where the academy-academy network was most prominent. The implication of this research outcome is that for regional innovation, government should more actively implement policies that can link academic institutes' knowledge to industry by expanding knowledge networks. In addition, we emphasize on the necessity of a regionally-appropriate policy, rather than a generalized industrial policy. And fundamentally, in regards to innovation, establishing a sound industrial infrastructure for regional development and efforts to link relevant actors are required.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.59-79
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• 2007

P2P (Peer to Peer) techniques have been well applied to file sharing due to its cost-effectiveness and convenience. Dynamic network evolution is another good thing for P2P according to addition and deletion of nodes and change of files a node has. Our research proposes a P2P-based KMS (Knowledge Management System). Knowledge of enterprises spreads all over sub-organizations like oversea factories and sales departments and is changed in dynamic manner. P2P techniques are, therefore well matched with knowledge management domain. In order to increase search efficiency, we introduce social network theory into P2P-based KMS. Social network technique makes the most similar nodes (in KMS domain, nodes which has the most similar knowledge) its own neighbors, which makes eventually search efficiency increase. We developed our prototype system P2P-SN-KMS and evaluated by simulation.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.95-115
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• 2001

We propose classifying the venture firms into four types of knowledge management. For this purpose, we collected questionnaire data from 101 venture firms listed in KOSDAQ, and applied a unsupervised neural network algorithm SOM to obtain four clusters representing knowledge management types-High Tech Type, Organizational Knowledge Type, Information Technology Type, and Beginner Type. Based on the results, we conclude that the venture firms listed in KOSDAQ should first know its own knowledge management type, and then apply appropriate strategies to take advantage of the knowledge management impacts on the competitiveness.

• Agribusiness and Information Management
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• v.3 no.1
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• pp.23-37
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• 2011

The development of informatization impacts all sectors, including agriculture. Agricultural informatization builds the knowledge linkage structures of agricultural innovation systems globally. This study investigated the global knowledge linkage structures in agricultural innovation pertinent to information technology (IT) for agricultural research and development (R&D) investments and activities. We explored the longitudinal trend of systemness within the networked research relationships in the triple helix (TH) of the university, industry and government (UIG). We collected data from publications in the Science Citation Index (SCI), the Social Sciences Citation Index (SSCI), and the Arts and Humanities Citation Index (A&HCI) to analyze the TH network dynamics. We also performed a scientometrics analysis to quantitatively identify the knowledge and insights of global agricultural innovation structures. These results could be informative for individual countries. Our findings reveal that the global knowledge linkage structures in the agricultural sector that are pertinent to IT fluctuate widely and fail to increase the capacity of agricultural innovation research due to a neglect of the network effects of the TH dynamics of UIG.