• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.111-114
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• 2004

Management of human knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. Artificial intelligence and knowledge engineering has provided some degree of rigor to the study of knowledge systems and expert systems(ES) re able to use knowledge to solve the problems and answer questions. Therefore, the process of conceptualization and inference of knowledge are fundamental problem solving activities and hence, are essential activities for solving the problem of software ES construction Especially, the access to relevant, up-to-date and reliable knowledge is very important task in the daily work of physicians and nurses. In this study, we propose the conceptualization and inference mechanism for implicit knowledge management in medical diagnosis area. To this purpose, we combined the dynamic knowledge map(KM) and relational database(RDB) into a dynamic knowledge map(DKM). A graphical user-interface of DKM allows the conceptualization of the implicit knowledge of medical experts. After the conceptualization of implicit knowledge, we developed an RDB-based inference mechanism and prototype software ES to access and retrieve the implicit knowledge stored in RDB. Our proposed system allows the fast comfortable access to relevant knowledge fitting to the demands of the current task.

• Journal of Korean Elementary Science Education
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• v.21 no.1
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• pp.13-24
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• 2002

This study examined the knowledge structure constructed by children before formal instruction, and successive changes in the structural complexity of knowledge during the learning of the 'Structure and Function of Plants' unit. The researchers let the 5th graders draw the first draft of their concept maps in order to examine the pre-existing knowledge structure concerned with the unit and also four concept maps after completing every fourth lesson. Each concept map drawn by children on the basis of the previous one showed the degree of their current understanding on the structure and function of plants. The results revealed that only two levels of hierarchy and five relationships among the components of the first concept map(relationship, hierarchy, cross link and example) were proven to be valid in terms of conceptual relevance. According to the standard map, there was no change in hierarchy from the 2nd to the 3rd map, and in example from the 2nd to the 4th map. However, the gradual and successive increases of the scores in all components appeared in the children's maps throughout the unit. Knowledge restructuring occurred strongly in the early periods from the 1st to the 6th lesson, and the significant stable changes in tuning and accretion appeared throughout the whole lessons. The results also showed that there were no significant gender differences on the 5th grader's knowledge structuring.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.9-12
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• 2004

Developing an efficient knowledge base construction mechanism as an input method for expert systems (ES) development is of extreme importance due to the fact that an input process takes a lot of time and cost in constructing an ES. Most ES require experts to explicit their tacit knowledge into a form of explicit knowledge base with a full sentence. In addition, the explicit knowledge bases were composed of strict grammar and keywords. To overcome these limitations, this paper proposes a knowledge conceptualization and construction mechanism for automated knowledge acquisition, allowing an efficient decision. To this purpose, we extended traditional knowledge map (KM) construction process to dynamic knowledge map (DKM) and combined this algorithm with relational database (RDB). In the experiment section, we used medical data to show the efficiency of our proposed mechanism. Each rule in the DKM was characterized by the name of disease, clinical attributes and their treatments. Experimental results with various disease show that the proposed system is superior in terms of understanding and convenience of use.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.103-123
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• 2006

Assuming that the knowledge creation and retrieval functions could be the most important factors for a successful knowledge management(KM) especially during the promotion stage of KM, this study suggests an e-learning application as one of best methods for producing knowledge and also the integrated knowledge repository model in which learning, knowledge. and information objects can be semantically associated through topic map-based knowledge map. The traditional KM system provides a simple directory-based knowledge map. which can not provide the semantic links between topics or objects. The proposed model can be utilized as a solution to solve the above-mentioned disadvantages of the traditional models. In order to collect the basic data for the proposed model, first, case studies utilizing interviews and surveys were conducted targeting at three Korean insurance companies' knowledge managers(or e-learning managers) and librarians. Second, the related studies and other topic map-based pilot systems were investigated.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.251-260
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• 2016

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patent, and project reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer the further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a RDB-to-Triples transformer is implemented. Lastly, we show an experiment on R&D data integration using the lightweight ontology, triples generation, and visualization and navigation of the knowledge map.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.961-966
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• 2012

In this paper, we analyze a main patent positioning to analyze data based on patent application as stage of data collection to organize knowledge map of LED through patent application. We also analyze the present condition of patent application for technology sector. We propose basic data to make knowledge map through the analysis of technical distribution for applicant by each country.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.568-571
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• 2004

In this study, we propose an efficient expert system (ES) construction mechanism by using dynamic knowledge map (DKM) and database management systems (DBMS). Generally, traditional ES and ES developing tools has some limitations such as, 1) a lot of time to extend the knowledge base (KB), 2) too difficult to change the inference path, 3) inflexible use of inference functions and operators. First, to overcome these limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. Then, elation database (RDB) and its management systems will help to transform the relationships from diagram to relational table. Therefore, our mechanism can help the ES or KBS (Knowledge-Based Systems) developers in several ways efficiently. In the experiment section, we used medical data to show the efficiency of our mechanism. Experimental results with various disease show that the mechanism is superior in terms of extension ability and flexible inference.

• The Journal of Society for e-Business Studies
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• v.6 no.1
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• pp.101-121
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• 2001

MOCT(Ministry of Construction St Transportation) suggested "A Plan for Implementation of Knowledge-based Economy for the Construction Industry" to add high value to a construction industry. by means of knowledge-based management. However, there is no knowledge management system which considers characteristics of a domestic construction industry. So a useful knowledge vanished the moment a company terminated a construction Uoject. Therefore, it is necessary to develop "An application method of a knowledge management system for the construction industry" which takes a real environment into consideration. This study aims to present the road map of a knowledge management system which helps the bodies of the construction industry(government, research center/university, enterprise, etc) to do a knowledge activity efficiently. To accomplish this objective, we analyzed overall knowledge management activities. In addition we suggested an implementation direction and strategy of a knowledge management system for the construction industry. As results of this study, we presented the road map of a knowledge management system for the construction industry, which is composed of policy forum, study forum, professional forum, research forum and knowledge 8t information DB. We expect that results of this study will be used as a basis for implementing the knowledge portal that brings a construction industry to a high value added industry.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.101-107
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• 2006

In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.