• Title/Summary/Keyword: Knowledge structure

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The Impact on Structures of Knowledge Creation and Sharing on Performance of Open Collaboration: Focus on Open Source Software Development Communities (개방형협업 참여자의 지식창출·지식공유 구조와 혁신 성과: 오픈소스 소프트웨어 개발 커뮤니티를 중심으로)

  • Koo, Kyungmo;Baek, Hyunmi;Lee, Saerom
    • Knowledge Management Research
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    • v.18 no.4
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    • pp.287-306
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    • 2017
  • This research focus on the effect of developers' participation structure in knowledge creation and knowledge sharing activities in open source software development projects. Based on preferential selection theory, hypotheses of relationship between a developers' concentration of knowledge creation/sharing activities and collaboration performance was derived. To verify the hypotheses, we use the Gini coefficient in the commit contribution of the developers (knowledge creation) and the centralization index in the repository issue network (knowledge sharing network). Using social network analysis, this paper calculates centralization index from developers in the issue boards in each repository based on data from 837 repositories in GitHub, a leading open source software development platform. As a result, instead of all developers creating and sharing knowledge equally, only a few of developers creating and sharing knowledge intensively further improve the performance of the open collaboration. In other words, a few developers predominantly providing commit and actively responding to issues raised from other developers enhance the project performance. The results of this study are expected to be used by developers who manage open source software project as a governance strategy, which could improve the performance of open collaboration.

A Study on the Knowledge Structure Networks of International Collaboration in Psychiatry (정신의학 분야 국제공동연구의 지식구조 네트워크에 관한 연구)

  • Kim, Eun-Ju;Nam, Tae-Woo
    • Journal of the Korean Society for information Management
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    • v.32 no.3
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    • pp.317-340
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    • 2015
  • This study clarified the knowledge structure of international collaboration in psychiatry based on analyzing networks in order to construct cooperation networks for international collaboration in psychiatry in South Korea. The result of analysis of knowledge structure at a state-level is as follows. First, this study found that the rate of collaboration for five years is high as 89.97%. Moreover, this study investigated the change of rate of collaboration and international collaboration according to the passage of time, and ascertained that while the rate of international collaboration has increased, Second, this study examined the trend of research on collaboration between Asian countries, and found that collaboration between Asian countries is on a low level. Third, the country (or group) that the number of papers of international collaboration and the value of centrality are the highest is EU-28. The result of analysis of knowledge structure at a research output-level is as follows. this study analyzed the correlation of centrality with research output, and found that positive correlation exists in the three indicators of centrality, and a country with high centrality has good research output.

A Study on Updating the Knowledge Structure Using New Topic Detection Methods (새로운 주제 탐지를 통한 지식 구조 갱신에 관한 연구)

  • Kim, Pan-Jun;Chung, Young-Mee
    • Journal of the Korean Society for information Management
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    • v.22 no.1 s.55
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    • pp.191-208
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    • 2005
  • This study utilizes various approaches for new topic detection in the process of assigning and updating descriptors, which is a representation method of the knowledge structure. Particularly in the case of occurring changes on the knowledge structure due to the appearance and development of new topics in specific study areas, new topic detection can be applied to solving the impossibility or limitation of the existing index terms in representing subject concepts. This study confirms that the majority of newly developing topics in information science are closely associated with each other and are simultaneously in the phase of growth and development. Also, this study shows the possibility that the use of candidate descriptor lists generated by new topic detection methods can be an effective tool in assisting indexers. In particular. the provision of candidate descriptor lists to help assignment of appropriate descriptors will contribute to the improvement of the effectiveness and accuracy of indexing.

Knowledge Structure of Posttraumatic Growth Research: A Network Analysis (네트워크 분석을 통한 외상 후 성장 지식구조 연구)

  • Shin, JooYeon;Kwon, Sunyoung;Bae, Ka Ryeong
    • Journal of Industrial Convergence
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    • v.20 no.10
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    • pp.61-69
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    • 2022
  • Posttraumatic growth literature has been rapidly expanding in multiple academic disciplines. Purpose of this study is to examine the knowledge structure of posttraumatic growth utilizing a network analysis. Papers published between 1996 and 2018 were searched on the Web of Science, focusing on terms related to posttraumatic growth. One thousand six-hundred and fifty-nine keywords were published 6,343 times in 1,780 papers; thus, a total of 322 keywords (5,195 appearances) were selected for the final analysis. The network analysis and network visualization tool used were NodeXL and PFnet, respectively. The keywords which appeared the most frequently were "Posttraumatic growth," followed by "Posttraumatic Stress Disease," "Cancer," and "Trauma." A total of 322 nodes have been reduced to 175 nodes and divided into a total of five groups. The five groups were "Posttraumatic Growth in Cancer, Chronic/Serious Illness, and Disability," "Posttraumatic Growth-related Psychological Variables and Psychotherapy," "Posttraumatic Growth in the Context of Death," "Cognitive Mechanisms of Posttraumatic Growth," and "Vicarious Posttraumatic Growth." This study provides a systematic overview on the knowledge structure of posttraumatic growth by quantitatively network analysis.

Exploring Influence of Network Structure, Organizational Learning Culture, and Knowledge Management Participation on Individual Creativity and Performance: Comparison of SI Proposal Team and R&D Team (네트워크 구조와 조직학습문화, 지식경영참여가 개인창의성 및 성과에 미치는 영향에 관한 실증분석: SI제안팀과 R&D팀의 비교연구)

  • Lee, Kun-Chang;Seo, Young-Wook;Chae, Seong-Wook;Song, Seok-Woo
    • Asia pacific journal of information systems
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    • v.20 no.4
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    • pp.101-123
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    • 2010
  • Recently, firms are operating a number of teams to accomplish organizational performance. Especially, ad hoc teams like proposal preparation team are quite different from permanent teams like R&D team in the sense of how the team forms network structure and deals with organizational learning culture and knowledge management participation efforts. Moreover, depending on the team characteristics, individual creativity will differ from each other, which will lead to organizational performance eventually. Previous studies in the field of creativity are lacking in this issue. So main objectives of this study are organized as follows. First, the issue of how to improve individual creativity and organizational performance will be analyzed empirically. This issue will be performed depending on team characteristics such as ad hoc team and permanent team. Antecedents adopted for this research objective are cultural and knowledge factors such as organizational learning culture, and knowledge management participation. Second, the network structure such as degree centrality, and structural hole is used to analyze its influence on individual creativity and organizational performance. SI (System Integration) companies are facing severely tough requirements from clients to submit very creative proposals. Also, R&D teams are widely accepted as relatively creative teams because their responsibilities are focused on suggesting innovative techniques to make their companies remain competitive in the market. SI teams are usually ad hoc, while R&D teams are permanent on an average. By taking advantage of these characteristics of the two kinds of teams, we will prove the validity of the proposed research questions. To obtain the survey data, we accessed 7 SI teams (74 members), and 6 R&D teams (63 members), collecting 137 valid questionnaires. PLS technique was applied to analyze the survey data. Results are as follows. First, in case of SI teams, organizational learning culture affects individual creativity significantly. Meanwhile, knowledge management participation has a significant influence on Individual creativity for the permanent teams. Second, degree centrality Influences individual creativity significantly in case of SI teams. This is comparable with the fact that structural hole has a significant impact on individual creativity for the R&D teams. Practical implications can be summarized as follows: First, network structure of ad hoc team should be designed differently from one of permanent team. Ad hoc team is supposed to show a high creativity in a rather short period, implying that network density among team members should be improved, and those members with high degree centrality should be encouraged to show their Individual creativity and take a leading role by allowing them to get heavily engaged in knowledge sharing and diffusion. In contrast, permanent team should be designed to take advantage of structural hole instead of focusing on network density. Since structural hole can be utilized very effectively in the permanent team, strong arbitrators' merits in the permanent team will increase and therefore helps increase both network efficiency and effectiveness too. In this way, individual creativity in the permanent team is likely to lead to organizational creativity in a seamless way. Second, way of Increasing individual creativity should be sought from the perspective of organizational culture and knowledge management. Organization is supposed to provide a cultural atmosphere in which Innovative idea suggestions and active discussion among team members are encouraged. In this way, trust builds up among team members, facilitating the formation of organizational learning culture. Third, in the ad hoc team, organizational looming culture should be built such a way that individual creativity can grow up fast in a rather short period. Since time is tight, reasonable compensation policy, leader's Initiatives, and learning culture formation should be done In a short period so that mutual trust is built among members quickly, and necessary knowledge and information can be learnt rapidly. Fourth, in the permanent team, it should be kept in mind that the degree of participation in knowledge management determines level of Individual creativity. Therefore, the team ought to facilitate knowledge circulation process such as knowledge creation, storage, sharing, utilization, and learning among team members, which will lead to team performance. In this way, firms must control knowledge networks in permanent team and ad hoc team in a way mentioned above so that individual creativity as well as team performance can be maximized.

Combining Faceted Classification and Concept Search: A Pilot Study

  • Yang, Kiduk
    • Journal of the Korean Society for Library and Information Science
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    • v.48 no.4
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    • pp.5-23
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    • 2014
  • This study reports the first step in the Classification-based Search and Knowledge Discovery (CSKD) project, which aims to combine information organization and retrieval approaches for building digital library applications. In this study, we explored the generation and application of a faceted vocabulary as a potential mechanism to enhance knowledge discovery. The faceted vocabulary construction process revealed some heuristics that can be refined in follow-up studies to further automate the creation of faceted classification structure, while our concept search application demonstrated the utility and potential of integrating classification-based approach with retrieval-based approach. Integration of text- and classification-based methods as outlined in this paper combines the strengths of two vastly different approaches to information discovery by constructing and utilizing a flexible information organization scheme from an existing classification structure.

Knowledge Structure for Cost Estimates Based on Standardized Cost Database (원가산정을 위한 표준분류체계 활용한 지식체계 개발)

  • Im, Haekyung;Kang, Namhee;Choi, Jaehyun
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2016.05a
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    • pp.235-236
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    • 2016
  • The importance of construction management has been increasing due to the fact that complex construction projects blend several different industries depending on the traits of the construction. This research was conducted to search for a method to enhance efficiency in cost management of construction project and meet the need for reusability of accumulated construction information. The process of detailed estimation and methodology for using standard unit price information has been developed to strengthen the interoperability in cost information by utilizing a standard classification system. The concept of ontology is proposed as a method of connecting construction information based on a standard breakdown structure to increasing the connectivity of the cost information in the construction project. Therefore, construction information knowledge framework is developed in order to improve the efficiency of the detailed estimation work process.

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A Study on the Metadata Modeling for Research Result Information Using RDF/RDFS (RDF/RDFS를 이용한 연구성과물정보 메타데이터 모델링에 관한 연구)

  • Park, Dong-Jin
    • 한국디지털정책학회:학술대회논문집
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    • 2005.11a
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    • pp.383-389
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    • 2005
  • The purpose of this paper is to develop the metadata on the information of research result in Science and technology and to design the domain knowledge structure using semantic web technology for further implementation. In this paper, we first analyze the existing theories and techniques related to the metadata in such fields as R&D research result, international standard, and semantic web. Then, we extract and group the relevant factors from Dublin Core, CERIF, and the research results for building the integrated metadata framework. Based on our proposed metadata, we design a domain knowledge structure which employs RDF/RDFS as knowledge representation tool. Therefore, we can implement the ontology which produce the 'intelligent' information service and improve the interoperability between the research institutions. Also, the metadata can be used as the basis for developing National R&D Performance Information, and in terms of research institutions, can be used as tools for managing the their own research results information systematically and consistently.

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A SHdy on the Development of an Expert System for Chemical Plant Diagnosis Fault -An Object Description System based on Functional Structure- (화학 플랜트의 고장원 탐색 전문가 시스템에 관한 연구 -기능구조에 의한 대상의 지식표현 방법-)

  • 황규석
    • Journal of the Korean Society of Safety
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    • v.7 no.2
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    • pp.14-23
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    • 1992
  • A methodology for developing an object description system based on functional-structure of chemical plant is proposed. A knowledge base for chemical plant fault diagnosis is also organized in a generic fashion using the heuristic knowledge of human operators. A plant can be seen as a hierarchical set of subsystems. Each subsystem is called a SCOPE. The state of the plant and the behavior of each subsystem is managed by the SCOPES. A computer-based system based on thls methodology and knowledge base has been developed and applied to the subprocess of ethylene plant to evaluate the effectiveness of the methodology.

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Comparison of Alternative knowledge Acquisition Methods for Allergic Rhinitis

  • Chae, Young-Moon;Chung, Seung-Kyu;Suh, Jae-Gwon;Ho, Seung-Hee;Park, In-Yong
    • Journal of Intelligence and Information Systems
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    • v.1 no.1
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    • pp.91-109
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    • 1995
  • This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

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