• 지식경영연구
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• 제21권1호
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• pp.117-136
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• 2020

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

• International Journal of Safety
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• 제4권2호
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• pp.12-17
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• 2005

This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

• Journal of Information Technology Applications and Management
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• 제28권6호
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• pp.117-132
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• 2021

Based on the core value of the Q&A community, the contribution of knowledge and information has a great impact on users' community evaluation. As a small social group, the relationships and interactions among community members are quickly formed through information technology. As such, the cognitive evaluation of the relationship between community members will have an impact on the intention of information contribution. This research builds on the previous research based on the social exchange theory and establishes a dual model of swift guanxi in examining the relationship between guanxi and continuous knowledge contribution. In the current study, 305 survey questionnaires were used and 249 valid questionnaires were used for analysis. The analysis results are as follows: First, information support has a positive impact on dedication-based swift guanxi. While hypothesis between information support and constraint-based swift guanxi was not be supported. Second, emotional support has a positive impact on the formation of swift guanxi from a dual perspective. Third, the swift guanxi from the dual perspective has a positive impact on the intention of continuous knowledge contribution. Finally, although personal involvement has an adjustment effect, it is a downward adjustment effect, hypotheses are not supported. The current study offers theoretical and practical implications in field of knowledge management, specifically knowledge contribution in the virtual community.

• 대한조선학회논문집
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• 제29권1호
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• pp.1-13
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• 1992

본 논문은 선박의 초기설계와 중앙단면 설계용 지식베이스 시스템을 개발하고 지식공학이 선체구조 설계 분야에 매우 효율적으로 적용될 수 있음을 검토하는데 그 목적이 있다. 이를 위하여 첫째 구조설계에 적합한 지식베이스 전문가 시스템 개발용 도구(tool)인 E.1을 개발하였다. E.1은 C언어로 작성되었고 구조설계 과정을 효율적으로 뒷받침해주기 위해서 자동 반복 계산과 재추론 기능을 갖고 있다. 둘째 살물선의 초기설계시 주요치수 추정과 중앙단면 설계를 위한 지식베이스를 개발하였다. 이 지식베이스들은 통계적 경험식, 선급 규정, 경험적 지식 등을 rule, fact, table로 표현하여 구성하였다. 셋째 지식베이스의 추론을 통하여 중앙 단면 최적 설계 프로그램의 입력 자료를 생성하고 최적화 프로그램을 수행시키는 통합(hybrid) 시스템을 구축하였다. 넷째 보강판 설계용 지식베이스 시스템을 개발하기 위해 최적화 결과의 회귀분석을 이용하는 간이 설계법을 적용해 보았다. 이 시스템을 사용하여 살물선과 보강판의 설계과정을 보이고 그 결과를 검토해 보았다. 앞으로 E.I와 성능 개선과 더 우수한 지식베이스가 축적됨에 따라서 선체 구조설계에 사용되는 기존의 CAD 시스템의 성능이 크게 보완될 것으로 기대된다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• 한국문헌정보학회지
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• 제40권1호
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• pp.397-416
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• 2006

이 글은 지식기반 사회의 역동적 엔진으로서 다양한 유형의 지식을 제공할 수 있는 통합형 지식체계의 구현을 도서관의 기본 역할로 보고, 이를 위한 지식관리 모형을 지식생태학 관점을 통해 제시한 것이다. 지식생태학 관점에서 접근한 지식관리는 지식생태계를 구성하는 사회적 요소간의 상호작용을 통해 성장하는 유기체로서 활동하는 도서관의 논리적 물리적 역할을 의미한다. 도서관은 지식생태계의 구심점으로서 지식조정자 역할을 담당하는 정보전문가를 통해 지식생산자가 지식을 제대로 생산 배포할 수 있도록 지원하고. 적절한 지식이 지식소비자에게 적시에 전달되고 재생산되는 선순환 체계를 주도해 감으로써 지식생태계를 활성화하는 지식관리 체계를 구현하여야 한다.

• 지식경영연구
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• 제3권1호
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• pp.75-91
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• 2002

This research is interested in organization members' knowledge contribution, along with the requirement for the effective knowledge management as a critical corporate asset. We consider the performance appraisal and reward system on knowledge sharing as a key issue for the successful knowledge management. Analyzed will be the interactive relationship among the performance appraisal and reward system, individual knowledge contribution, and organizational knowledge contribution effectiveness. This case study is based on in-depth interviews in the consulting industry recognized as a knowledge-integrated industry. The purpose of this research is to examine how firms evaluate and reward organization members' knowledge contribution, to define how fim1s utilize IT for the knowledge management, and to show how the performance appraisal and reward system influence organizational knowledge contribution effectiveness. Besides, other determinants for knowledge contribution effectiveness are defined. It is recognized that knowledge contribution effectiveness is positively related to non-monetary rewards and informal appraisals. As for the future study, we recommend the empirical research based on several propositions developed in this study.

• 지식경영연구
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• 제12권4호
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• pp.55-75
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• 2011

Scant attention has been given to analyzing how knowledge sourcing strategies affect firm performance in SMEs and what are the differences between SMEs and large firms in the patterns of knowledge sourcing strategies adoption. This study attempts to advance the current literature by examining the impact of knowledge sourcing strategies on SMEs performance. The empirical segment of our work is based on data on knowledge sourcing strategies of SMEs and organizational performance from a sample of 166 Korean firms. Our results indicate knowledge sourcing adoption patterns of SMEs are different from large firms. In addition, the results confirm that substitutability between internal- and external-oriented, person- and external-oriented sourcing strategies. This study sheds new light on knowledge management (KM) research by identifying the relationship between knowledge sourcing strategies and SMEs performance.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.185-191
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• 2006

As development of information technology, companies stress the need of knowledge management. Companies construct ERP system including knowledge management. But, it is not easy to formalize knowledge in organization. They experience that constructing information system help knowledge management. Now, we focus on engineering knowledge. Because engineering data contains experts' experience and know-how in its own, engineering knowledge is a treasure house of knowledge. Korean shipyards are leader of world shipbuilding industry. They have accumulated a store of knowledges and data. But, they don't have data minning tool to utilize accumulated data. This paper treats development of data minning tools for the utilization of shipbuilding knowledge based on genetic programming (GP).

• 지식경영연구
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• 제6권2호
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• pp.149-168
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• 2005

Owing to the limits of IT System-driven knowledge management(KM) for innovation processes, alternative KM methods has been suggested such as: (1) the knowledge network of experts or (2) communities-of-practice. This study analyzes two cases in terms of on-line expert knowledge networks for problem-solving, with the dimensions of analysis based on a theoretical framework. By analyzing the cases of S company's expert network and Naver's Ji-sik-iN, we found that system quality(e.g., ease of use, accessibility, and searching function), information/knowledge quality(e.g., usefulness, accuracy, and timeliness), knowledge-sharing culture, social capital and relevant reward systems are important for stimulating a Q&A-based problem-solving knowledge network. Implications of the findings and future research directions are discussed.