• 대한안전경영과학회지
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• 제6권1호
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• pp.201-217
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• 2004

In this research, OIL (Ontology Inference Layer), one of the ontology language, is applied for classifying product data systematically, defining concepts, and establishing relationship between concepts. By transforming steel product data into XML documentation and managing them, knowledge management based on the logical structure of documents is possible.

• 지능정보연구
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• 제5권1호
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• pp.13-34
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• 1999

Building a knowledge base effectively has been an important research area in the expert systems field. A variety of approaches have been studied including rules, semantic networks, and frames to represent the knowledge base for expert systems. As the size and complexity of the knowledge base get larger and more complicated, the integration of knowledge based with database technology cecomes more important to process the large amount of data. However, relational database management systems show many limitations in handing the complicated human knowledge due to its simple two dimensional table structure. In this paper, we propose Object-Oriented Knowledge Store (OOKS), a knowledge base model on the basis of a frame sturcture using an object-oriented database. In the proposed model, managing rules for inferencing and facts about objects in one uniform structure, knowledge and data can be tightly coupled and the performance of reasoning can be improved. For building a knowledge base, a knowledge script file representing rules and facts is used and the script file is transferred into a frame structure in database systems. Specifically, designing a frame structure in the database model as it is, it can facilitate management and utilization of knowledge in expert systems. To test the appropriateness of the proposed knowledge base model, a prototype system has been developed using a commercial ODBMS called ObjectStore and C++ programming language.

• 경영과학
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• 제11권2호
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• pp.65-77
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• 1994

One of the major issues in the implementation and maintenance of CIM databases is the sharing and exchange of information among the heterogeneous databases. This paper addresses some architectural aspects for integrating the heterogeneous multi-databases using knowledge-based expert systems. we propose a loosely integrated coupling system between databases and knowledge-based expert systems. Especially we suggest the architectural aspects of such a coupling methodology. we also present the structure and knowledge representation scheme for the proposed knowledge-based expert system. A prototype example is included to illustrate the framework and its mechanism for implementation.

• 정보관리학회지
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• 제20권1호
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• pp.341-371
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• 2003

본 연구는 한의학 처방 지식관리시스템 설계에 요구되는 사항들이 온톨로지의 추상적 개념구조 를 기반으로 하는 용어의 개념, 속성, 관계의 명확한 정의를 통해 더욱 합리적이고 효과적으로 실 현된다는 것을 전제로 하였다. 이에 따라 실세계 개념 모델링 방식으로 한의학 처방지식 온톨로 지를 개발하여 Protege-2000을 기반으로 온툴로지 시스템을 구축하였고, 시스템을 응용할 수 있 는 마크업 언어의 설계와 편집기를 만들어 지식의 추론이 가능한 한의학 처방 지식관리시스템을 구현하였다. 본 연구에서 구현한 시스템은 XML 기반의 RDF와 온톨로지 기술에 기반을 두고 있 으므로 차세대 인터넷 기술인 의미웹과의 연옹이 가능하다.

• 한국IT서비스학회지
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• 제17권2호
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• pp.143-163
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• 2018

The business models has a great impact on the successful management of enterprises. Business environment has been shifting from industrial economy to knowledge-based economy. Enterprises go through numerous trials for successful management in the changing environment. Along with trial tests, research areas have been growing simultaneously. Although many researches have been conducted with regard to business models, it is very insufficient to systematically analyze the knowledge flow of research. Accordingly, successive researchers who want to study the business model may find it difficult to establish the orientation of future application research based on understanding the process of changing the knowledge structure that have accumulated so far. This study is intended to determine the current state of the business model research and to understand the process of knowledge structure changes in keywords that appear in 2,667 business model articles in the SCOPUS database. Identifying the knowledge structure has been completed through social network analysis, a methodology based on the 'relationship', and the changes in the knowledge structure were identified by classifying them into four different periods. The analysis showed that, first, the number of business model co-author increases over time with the need for academic diversity. Second, the 'innovation' keyword has the biggest center in the network, and over time, the lower-rank keyword which was in the former period has emerged as the top-rank keyword. Third, the cohesiveness group decreased from 12 before 2000 to 5 in 2015 and also the modularity decreased as well. Finally, examining characteristics of study area through a cognitive map showed that the relationships between domains increased gradually over time. The study has provided a systematic basis for understanding the current state of the business model research and the process of changing knowledge structure. In addition, considering that no research has ever systematically analyzed the knowledge structure accumulated by individual researches, it is considered as a significant study.

• 정보관리학회지
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• 제28권1호
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• pp.145-170
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• 2011

본 연구는 연구문헌의 지식구조를 반영하는 의미기반 지식조직체계의 실험적 모형을 제시하는 것을 목적으로 한다. 이를 위해 한국연구재단의 기초학문자료센터에 대한 사례분석을 하였다. 기초학문자료센터 연구성과물 DB와 학술용어 DR의 개념클래스 및 인스턴스를 대상으로 연구문헌의 지식구조를 파악하였으며, 기초학문자료센터 시스템의 학술적 이해형성 기능을 분석하였다. 또한 연구문헌의 지식구조와 색인어의 관계를 분석하였다. 이러한 분석을 통해 지식구조와 색인어의 관계구조, 26개의 연구문헌 지식구조 공리 및 11개의 의미관계 추론규칙으로 구성되는 온톨로지 모형, 즉 연구문헌의 지식구조와 그 의미관계에 의한 실험적 지식조직체계 모형을 제시하였다.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.245-252
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• 2022

Risk identification for bridge projects is a knowledge-based and labor-intensive task involving several procedures and stakeholders. Presently, risk information of bridge projects is unstructured and stored in different sources and formats, hindering knowledge sharing, reuse, and automation of the risk identification process. Consequently, there is a need to develop structured and formalized risk information for bridge projects to aid effective risk identification and automation of the risk management processes to ensure project success. This study proposes a semantic risk breakdown structure (SRBS) to support risk identification for bridge projects. SRBS is a searchable hierarchical risk breakdown structure (RBS) developed with python programming language based on a semantic modeling approach. The proposed SRBS for risk identification of bridge projects consists of a 4-level tree structure with 11 categories of risks and 116 potential risks associated with bridge projects. The contributions of this paper are threefold. Firstly, this study fills the gap in knowledge by presenting a formalized risk breakdown structure that could enhance the risk identification of bridge projects. Secondly, the proposed SRBS can assist in the creation of a risk database to support the automation of the risk identification process for bridge projects to reduce manual efforts. Lastly, the proposed SRBS can be used as a risk ontology that could aid the development of an artificial intelligence-based integrated risk management system for construction projects.

• 한국전자거래학회:학술대회논문집
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• 한국전자거래학회 2005년도 e-Biz World Conference 2005
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• pp.24-50
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• 2005

Knowledge management is a current management concept dealing with information gathering and implementation processes for the organizational performance advantage. The study investigates knowledge management in two perspectives: internally, Organizational information processing structure and externally, strategic issue management system. The study proposes the existence of filtering processes in the organizations. Based on this argument, the authors propose that organizations need to employ strategic issue management system for the successful implementation of knowledge management.

• 지식경영연구
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• 제14권4호
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• pp.75-86
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• 2013

The deeping interests and research toward Entrepreneurship, which is considered as an potential alternative for solving the continuing economic recession in the $21^{st}$ century, have grown. The process and methodology of the research could not be systematically arranged and the results of the research lack in efforts on the application of increasing suceess ratio in starting new business. This study adopted corpus methodology, through which we try to analyzes the knowledge structure in entrepreneurship research, derive essential concepts and the consisting domains in venture research. Based on the results of analysis, this study constructs the knowledge structure of venture research in a form of knowledge ontology. The results of the study could be a ground for entrepreneurship research and utilized as implication for a creation of construction for the entrepreneurship knowledge ontology.

• Journal of Information Technology Applications and Management
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• 제26권1호
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• pp.65-75
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• 2019

As the use of semantic web based on XML increases in the field of data management, a lot of studies to extract useful information from the data stored in ontology have been tried based on association rule mining. Ontology data is advantageous in that data can be freely expressed because it has a flexible and scalable structure unlike a conventional database having a predefined structure. On the contrary, it is difficult to find frequent patterns in a uniformized analysis method. The goal of this study is to provide a basis for extracting useful knowledge from ontology by searching for frequently occurring subgraph patterns by applying transaction-based graph mining techniques to ontology schema graph data and instance graph data constituting ontology. In order to overcome the structural limitations of the existing ontology mining, the frequent pattern search methodology in this study uses the methodology used in graph mining to apply the frequent pattern in the graph data structure to the ontology by applying iterative node chunking method. Our suggested methodology will play an important role in knowledge extraction.