• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.1-18
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• 2016

Although previous studies show that organizational culture and organizational communication could be influential factors in knowledge management and organizational performance, the more integrated research to indicate relationships among all of the factors is still needed. By indicating the inter-relationships among the factors and the direction of the influence, this paper suggests various ways to develop an integrated approach to the improvement of organizational performance through knowledge management and other related factors. Thus, the purpose of this study is to investigate the relationships among national culture, organizational culture, organizational communication, knowledge management, and organizational performance. Based on a comprehensive review of extant literature on the relationships among these factors, the relationships are summarized in the conceptual model. According to the model, organizational performance is influenced by knowledge management, organizational communication, and organizational culture, and knowledge management is influenced by organizational communication and organizational culture. Based on the conceptual model, implications for human resource (HR) researchers and practitioners seeking to optimally improve organizational performance are presented.

• Smart Media Journal
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• v.12 no.9
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• pp.89-94
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• 2023

Recommender systems provide users with the most favorable items by analyzing explicit or implicit feedback of users on items. Recently, as the size of deep-learning-based models employed in recommender systems has increased, many studies have focused on reducing inference time while maintaining high recommendation accuracy. As one of them, a study on recommender systems with a knowledge distillation (KD) technique is actively conducted. By KD, a small-sized model (i.e., student) is trained through knowledge extracted from a large-sized model (i.e., teacher), and then the trained student is used as a recommendation model. Existing studies on KD for recommender systems have been mainly performed only for implicit feedback settings. Thus, in this paper, we try to investigate the performance and accuracy when applied to explicit feedback settings. To this end, we leveraged a total of five state-of-the-art KD methods and three real-world datasets for recommender systems.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.19-30
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• 2016

This study analyzed relative importance among business model factors for improving business performance of Knowledge Service Enterprises using the Business Model methodology. It also compares and analyzes the relative importance of manufacturing enterprises by using the previous research conclusion. This study finds Product & Service factor(0.361) is the most important among Marketing(0.251), Financial aspects(0.234), and Infrastructure(0.154) are follows. For the sub factors, Value Proposition(0.254) is the most importance factors and Revenue Streams(0.154), and Key Activities(0.107), and Key Resources(0.100), and Channels(0.086) are follows. Also, The Marketing has higher relative importance for Manufacturing enterprises, whereas the Product&Service has higher relative importance for Knowledge Service Enterprises. It proves that there is a difference in the relative importance between Manufacturing Enterprises and Knowledge Service Enterprises. This study concludes the importance of business model factor is different for each respective industry. Therefore, it suggests to consider different industrial aspects when build the business model for each industry.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.42-53
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• 2023

The purpose of this study is to obtain basic data for constructing a modeling practice program integrated with meta-modeling knowledge by analyzing the cognition level for each meta-modeling knowledge components through modeling practice in the context of the chemistry discipline content. A chemistry teacher conducted inquiry-based modeling practice including anomalous phenomena for 16 students in the second year of a science gifted school, and in order to analyze the cognition level for each of the three meta-modeling knowledge components such as model variability, model multiplicity, and modeling process, the inquiry notes recorded by the students and observation note recorded by the researcher were used for analysis. The recognition level was classified from 0 to 3 levels. As a result of the analysis, it was found that the cognition level of the modeling process was the highest and the cognition level of the multiplicity of the model was the lowest. The cause of the low recognitive level of model variability is closely related to students' perception of conceptual models as objective facts. The cause of the low cognitive level of model multiplicity has to do with the belief that there can only be one correct model for a given phenomenon. Students elaborated conceptual models using symbolic models such as chemical symbols, but lacked recognition of the importance of data interpretation affecting the entire modeling process. It is necessary to introduce preliminary activities that can explicitly guide the nature of the model, and guide the importance of data interpretation through specific examples. Training to consider and verify the acceptability of the proposed model from a different point of view than mine should be done through a modeling practice program.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.6
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• pp.17-34
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• 2021

As knowledge management is recognized as an important factor for organizational performance, organizations are increasing their investment in knowledge management policies and technologies. The purpose of this study is to suggest positive and negative causes on the intention to share knowledge through a using knowledge management system(KMS) and to suggest the effect of organizational sharing climate. Research models and hypotheses were presented through previous studies, and 417 samples were obtained through the survey for employees of organizations that adopted a KMS. As a result of the analysis, usefulness and ease of use of the KMS had a positive effect on the intention to share knowledge, and task conflict and ambiguity had a negative effect. The knowledge sharing climate was found to be an antecedent for the technology acceptance model and task stress. In addition, task stress moderated the effect of usefulness and ease of use with the intention to share knowledge using KMS. The results suggested the direction to be pursued at the organizational level for the continuous use of KMS.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.1-7
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• 1997

The inference process for medical expert system is mostly formed by diagnostic knowledge on the if-then rule base. Oriental medicine diagnostic knowledge, however, may involve uncertain knowledge caused by ambiguous concept. In this paper, we analyze an oriental medicine diagnostic process by a rule-based inference system, and propose a method for representing and processing uncertain oriental medicine diagnostic knowledge using CLP( R ) which is a kind of constraint satisfaction program.

• Annual Conference of KIPS
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• 2019.05a
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• pp.239-241
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• 2019

지식과 정보의 중요성이 강조되는 지식기반사회에서는 지식재산권의 대표적인 유형인 특허의 중요성이 날로 높아지고 있고, 그 수 또한 급증하고 있다. 특허 문서의 효과적 검색과 이용을 위해서는 새롭게 출원되는 특허 문서의 체계적인 분류 작업이 선행되어야 하고, 따라서 방대한 양의 특허 문서를 자동으로 분류해주는 시스템이 필요하다. 본 연구에서는 Doc2Vec 모델을 이용하여 국내 특허 문서의 특징(feature)을 추출하고, 추출된 특징을 바탕으로 한 특허 문서의 자동 분류 모형을 제안한다. 먼저 국내에 등록된 31,495 건의 특허 문서의 IPC(International Patent Classification)와 요약정보를 바탕으로 Doc2Vec 모델을 구축하였다. 구축된 Doc2Vec 모델을 통하여 훈련데이터의 특징을 추출한 후, 이 특징 벡터를 이용하여 분류기를 학습하였다. 마지막으로 Doc2Vec 모델을 이용하여 실험데이터의 특징 벡터를 추출하고 분류기의 성능을 실험한 결과, 43%의 분류 정확도를 얻었다. 이를 통해, 특허 문서 분류 문제에 Doc2Vec 모델의 사용 가능성을 확인할 수 있었다.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.301-305
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• 2023

최근 등장한 Large Language Models (LLM)은 자연어 처리 분야에서 눈에 띄는 성과를 보여주었지만, 주로 영어 중심의 연구로 진행되어 그 한계를 가지고 있다. 본 연구는 사전 학습된 LLM의 언어별 지식 전이 가능성을 한국어를 중심으로 탐구하였다. 이를 위해 한국어와 영어로 구성된 코드 스위칭 코퍼스를 구축하였으며, 기본 모델인 LLAMA-2와 코드 스위칭 코퍼스를 추가 학습한 모델 간의 성능 비교를 수행하였다. 결과적으로, 제안하는 방법론으로 학습한 모델은 두 언어 간의 희미론적 정보가 효과적으로 전이됐으며, 두 언어 간의 지식 정보 연계가 가능했다. 이 연구는 다양한 언어와 문화를 반영하는 다국어 LLM 연구와, 소수 언어를 포함한 AI 기술의 확산 및 민주화에 기여할 수 있을 것으로 기대된다.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.401-411
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• 2013

Selection of profitable research and development (R&D) projects is one of the major factors affecting sustained growth of firms and countries. This paper analyze what influences the knowledge on the business model exerted on selection of a R&D project. A business model converts the technology value to the customer value, and comprehensively describes the target customers for commercializing a new technology, core values, behaviors within organizations, resources, and external partners. Thus, understanding a business model would make R&D project evaluators place the feasibility and profitability of the business above the merits of the proposed technology in evaluating the technology development. To verify this hypothesis, we had 78 R&D project evaluators acquire the knowledge on the business model and measured how their criteria for R&D project selection have changed using the AHP method. The results shows that feasibility and profitability are more important than the merit of proposed technology, especially capability of company and business development are more important than the levels of technology innovation.

• Knowledge Management Research
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• v.22 no.3
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• pp.91-106
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• 2021

With the recent rapid development of deep learning technology, the demand for analyzing huge text documents in the national R&D field from various perspectives is rapidly increasing. In particular, interest in the application of a BERT(Bidirectional Encoder Representations from Transformers) language model that has pre-trained a large corpus is growing. However, the terminology used frequently in highly specialized fields such as national R&D are often not sufficiently learned in basic BERT. This is pointed out as a limitation of understanding documents in specialized fields through BERT. Therefore, this study proposes a method to build an R&D KoBERT language model that transfers national R&D field knowledge to basic BERT using further pre-training. In addition, in order to evaluate the performance of the proposed model, we performed classification analysis on about 116,000 R&D reports in the health care and information and communication fields. Experimental results showed that our proposed model showed higher performance in terms of accuracy compared to the pure KoBERT model.