• 지식경영연구
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• 제10권2호
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• pp.85-103
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• 2009

Web 2.0, characterized as openness, sharing, and participation, has enabled Internet users to easily generate a variety of contents, and to share them through Web 2.0 services. Knowledge search service (KSS) is positioning itself as one of typical Web 2.0 services. Yet, few studies have sharpened our understanding of users' knowledge sharing behavior in the KSS environment. In order to address this knowledge void, this paper attempts to explore antecedents of their usage behavior in the KSS environment. Typically, they utilize stored knowledge and share their knowledge simultaneously. Considering this characteristic, our study regards service usage intention and knowledge sharing intention as final dependent variables. This study advances a research model based on Bhattacherjee's expectation-confirmation model, which is expanded by incorporating trust belief. Because of the openness of KSS, trust belief is believed to play a critical role in forming users' service usage intention and knowledge sharing intention. Furthermore, this study examines the difference of the antecedents' effects in terms of users' gender. We collected data from 275 respondents who have experience in using KSS. PLS (partial least squares) was employed for the analysis of the data. Analysis results confirm the important role of user satisfaction and trust belief in the formation of users' service usage intention and knowledge sharing intention. More importantly, trust belief has a more salient effect on knowledge service usage intention than user satisfaction. This study also shows the moderating role of users' gender. Finally, this paper provides managerial guidance on strategic planning aimed at improving users' service usage intention and knowledge sharing intention in the KSS environment.

• 사회복지연구
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• 제41권1호
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• pp.281-306
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• 2010

본 연구는 사회복지담당공무원의 관계적 요인이 지식공유에 어떤 영향을 미치는지를 지식공유의도의 매개효과를 중심으로 살펴보았다. 이를 위해 대전광역시 사회복지담당공무원을 대상으로 설문조사를 실시하였고, 구조방정식모형을 통해 인과관계를 검증하였다. 지식공유에 영향을 미치는 관계적 요인으로는 신뢰, 의사소통의 개방성, 상호관계의 기대와 효능감을 설정하였고, 매개변수는 지식공유의도, 종속변수는 지식공유였다. 연구결과, 신뢰, 상호관계의 기대, 효능감이 지식공유의도에 긍정적 영향을 주었고, 지식공유에 긍정적 영향을 주는 요인은 효능감 뿐이었다. 지식공유의도도 역시 지식공유에 긍정적인 영향을 미치는 것으로 나타나 사회복지담담공무원의 지식공유는 주로 지식공유의도를 매개로 하여 이루어지는 것으로 나타났다. 이러한 연구결과에 근거하여 사회복지담당공무원들의 지식공유를 보다 활발히 하기 위한 방안에 대해 제언하였다.

• Asia pacific journal of information systems
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• 제22권3호
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• 대한인간공학회지
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• 제29권4호
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• 지식경영연구
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• 제22권3호
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• pp.91-106
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• 2021

최근 딥러닝 기술이 빠르게 발전함에 따라 국가 R&D 분야의 방대한 텍스트 문서를 다양한 관점에서 분석하기 위한 수요가 급증하고 있다. 특히 대용량의 말뭉치에 대해 사전학습을 수행한 BERT(Bidirectional Encoder Representations from Transformers) 언어모델의 활용에 대한 관심이 높아지고 있다. 하지만 국가 R&D와 같이 고도로 전문화된 분야에서 높은 빈도로 사용되는 전문어는 기본 BERT에서 충분히 학습이 이루어지지 않은 경우가 많으며, 이는 BERT를 통한 전문 분야 문서 이해의 한계로 지적되고 있다. 따라서 본 연구에서는 최근 활발하게 연구되고 있는 추가 사전학습을 활용하여, 기본 BERT에 국가 R&D 분야 지식을 전이한 R&D KoBERT 언어모델을 구축하는 방안을 제시한다. 또한 제안 모델의 성능 평가를 위해 보건의료, 정보통신 분야의 과제 약 116,000건을 대상으로 분류 분석을 수행한 결과, 제안 모델이 순수한 KoBERT 모델에 비해 정확도 측면에서 더 높은 성능을 나타내는 것을 확인하였다.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.800-806
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• 1999

This paper proposes IIKBAG(Intelligent Interlocking Knowledge Base Generator) which can build automatically the interlocking knowledge base utilized as the real-time interlocking strategy of the electronic interlocking system in order to enhance it's reliability and expansion. The IIKBAG consists of the inference engine and the knowledge base. The former has an auto-learning function which searches all the train routes for the given station model based on heuristic search technique while dynamically searching the model, and then generates automatically the interlocking patterns obtained from the interlocking relations of signal facilities on the routes. The latter is designed as the structure which the real-time expert system embedded on IS(Interlocking System) can use directly in order to enhances the reliability and accuracy. The IIKBAG is implemented in C computer language for the purpose of the build and interface of the station structure database. And, a typical station model is simulated to prove the validity of the proposed IIKBAG.

• 한국정보처리학회논문지
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• 제7권4호
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• pp.1150-1159
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• 2000

This paper presents an automatic text categorization model that improves the accuracy by combining statistical and knowledge-based categorization methods. In our model we apply knowledge-based method first, and then apply statistical method on the text which are not categorized by knowledge-based method. By using this combined method, we can improve the accuracy of categorization while categorize all the texts without failure. For statistical categorization, the vector model with Inverted Category Frequency (ICF) weighting is used. For knowledge-based categorization, Phrasal Patterns and Keyword Sets are introduced to represent sentence patterns, and then pattern matching is performed. Experimental results on new articles show that the accuracy of categorization can be improved by combining the tow different categorization methods.

• ETRI Journal
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• 제44권3호
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.54-60
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• 2021

Monte-Carlo GO is a computer GO program that is sufficiently competent without using knowledge expressions of IGO. Although it is computationally intensive, the computational complexity can be reduced by properly pruning the IGO game tree. Here, I achieve this by using a potential model based on the knowledge expressions of IGO. The potential model treats GO stones as potentials. A specific potential distribution on the GO board results from a unique arrangement of stones on the board. Pruning using the potential model categorizes legal moves into effective and ineffective moves in accordance with the potential threshold. Here, certain pruning strategies based on potentials and potential gradients are experimentally evaluated. For different-sized boards, including an official-sized board, the effects of pruning strategies are evaluated in terms of their robustness. I successfully demonstrate pruning using a potential model to reduce the computational complexity of GO as well as the robustness of this effect across different-sized boards.

• 한국컴퓨터정보학회논문지
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• 제23권6호
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• pp.9-14
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• 2018

This paper focuses on how the latest progress in digital media and technology affects the writing education environment. In the contemporary era, collecting numerous pices of information online, and arranging them to create new knowledge is important. There is also a need to seek new methods for writing education to stay in tune with the times. To that end, this paper suggests an open writing model using hypertexts. This writing model consists of a total of five stages, which are use of information, compilation of information, open mutual discussion, search of additional information and writing to recreate knowledge. The final outcome of such writing is writing using footnotes. By describing the gist of the keyword and adding numerous footnotes, such writing opens up an infinite possibility of re-creating information into new knowledge. This method can help university students who are accustomed to the digital society to proactively use information and improve multi-disciplinary communication skills required today. This author applied such a model to university writing education and found that more than 82% of the students were satisfied. Through the process of collaboration and recreation of knowledge in writing, learners found distinct benefits and noted their horizons had broadened. Given this effect, the open writing model using hypertexts is meaningful in that it forms a learning community that goes beyond a one-way feedback from instructor to student and instead nudges students to realize collective intellect. Moreover, it is meaningful in that it moves away from a top-down approach of the instructors passing down knowledge about writing and its rules, and towards a more proactive involvement by students in creating knowledge.