• Journal of the Korean Society for information Management
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• 제24권1호
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• pp.31-53
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• 2007

This paper presents a model of Task-Ontology for small unit operations(SUO) such as nongovernment organizations Despite the rapid development and extension of NGO in domestic area, most have insufficient structural domain resources in existence and underestimate the importance of information management. To improve the citizen's participation and to activate the conjoint actions among the NGO, which are critical to its social role-playing in global society, the modeling Task-Ontology is ultimately intended to implement the knowledge management system of NGO. In the perspective of ontology competency, not only the analysis of resources in vary, but also in-depth Interviews with the NGO practicing personnels and subject experts, and also the intensive observations of task-processing are required for the knowledge acquisition.

• Journal of the Korean Society for information Management
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• 제27권3호
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• pp.35-51
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• 2010

Emerging Web 2.0 services such as Twitter, Blogs, and Wikis alongside the poorlystructured and immeasurable growth of information requires an enhanced information organization approach. Ontology has received much attention over the last 10 years as an emerging approach for enhancing information organization. However, there is little penetration into current systems. The purpose of this study is to propose ontology implementation and methodology. To achieve the goal of this study, limitations of traditional information organization approaches are addressed and emerging information organization approaches are presented. Two ontology data models, RDF/OW and Topic Maps, are compared and then ontology development processes and methodology with topic maps based medical information retrieval system are addressed. The comparison of two data models allows users to choose the right model for ontology development.

• Journal of KIISE:Databases
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• 제35권2호
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• pp.155-168
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• 2008

This paper proposes a relational security model-based RDF Web ontology access control model. The Semantic Web is recognized as a next generation Web and RDF is a Web ontology description language to realize the Semantic Web. Much effort has been on the RDF and most research has been focused on the editor, storage, and inference engine. However, little attention has been given to the security issue, which is one of the most important requirements for information systems. Even though several researches on the RDF ontology security have been proposed, they have overhead to load all relevant data to memory and neglect the situation that most ontology storages are being developed based on relational database. This paper proposes a novel RDF Web ontology security model based on relational database to resolve the issues. The proposed security model provides high practicality and usability, and also we can easily make it stable owing to the stability of the relational database security model.

• Journal of the Korea Society of Computer and Information
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• 제15권5호
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• pp.1-11
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• 2010

The success of semantic web depends largely on the semantic annotation which systematizes knowledge for the construction and production of ontology. Therefore, the efficiency of semantic annotation is very important in order to change many knowledge expressions and generate into ontology instances. In this paper, we presents a generation system of rule-based ontology instances which are produced accurately and efficiently via semantic annotation in conventional web sites. In conventional studies, the manual process is necessary for finding relevant information, comparing it with ontology, and entering information. We propose a new method that manages keyword data regarding extracted information and rule information separately. Thus, it is quite practical to extract information efficiently from various web documents by adding a small number of keywords and rules. The proposed method shows the possibility of ontology instance generation which reuses the rules and keywords from the various websites.

• Journal of Intelligence and Information Systems
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• 제13권4호
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• pp.91-104
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• 2007

Ontology matching technique is one of the most important techniques in the Semantic Web as well as in other areas. Ontology matching algorithm takes two ontologies as input, and finds out the matching relations between the two ontologies by using some parameters in the matching process. Ontology matching is very useful in various areas such as the integration of large-scale ontologies, the implementation of intelligent unified search, and the share of domain knowledge for various applications. In general cases, the performance of ontology matching is estimated by measuring the matching results such as precision and recall regardless of the requirements that came from the matching environment. Therefore, most research focuses on controlling parameters for the optimization of precision and recall separately. In this paper, we focused on the harmony of precision and recall rather than independent performance of each. The purpose of this paper is to propose a methodology that determines parameters for the desired ratio of precision and recall that is appropriate for the requirements of the matching environment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.183-187
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• 2008

본 논문은 유전자의 기능이 비슷한 정도에 따른 사전정보의 값을 부여하며, 클러스터링시 사전정보를 활용할 수 있는 방법을 제시한다. 실세계 문제인 유전자는 각기 다양한 기능을 하는 특징적인 것으로 사전정보의 형태를 1과 0등으로 구분하던 과거의 방식으로는 정의하기가 어렵다. 유전자간의 비슷한 정도에 따라 사전정보의 값이 정해져야 하는 것은 필요하며, 이는 생물학자가 구축해놓은 유전자 온톨로지의 분석을 통하여 산출한다. 유전자 온톨로지는 기능별 카테고리로 분류하며, 세부 기능은 하위의 카테고리로 형성된 거대한 트리 구조의 형태를 띤다. 온톨로지 분석을 통해 형성된 사전정보의 값은 0과 1사이의 연속적인 값으로 형성이 되며, 이 값은 클러스터링 과정 중 거리 계산에 활용함으로써, 그 결과의 성능이 우수함을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.891-894
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• 2021

온톨로지(Ontology)는 사람과 컴퓨터, 또는 컴퓨터 간의 개념 및 개념 표현을 공유하기 위한 개념화의 명시적 규약을 의미한다. 기존의 온톨로지 생성은 전문가에 의한 수작업에 의존되어 비용과 시간이 많이 드는 한계가 있다. 이에 본 논문에서는 딥러닝(Deep learning)기반의 기계번역 개념을 적용한 사례를 활용하여, 수작업의 의존성이 감소한 방법으로 텍스트로부터 온톨로지를 생성하는 방법을 구현하였다. 특히 기존 연구에서 제안한, 딥러닝을 이용해 텍스트로부터 지식 표현 시퀀스를 추출한 정보를 활용하여, 지식 표현 구조를 온톨로지로 변환하고 지식 베이스로 확장하는 과정을 통해 자동화 된 Text-to-Ontology 변환 방법론을 제안하고자 한다.

• Proceedings of the Korean Society for Language and Information Conference
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• 한국언어정보학회 2008년도 정기학술대회
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• pp.77-84
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 한국정밀공학회 2003년도 추계학술대회 논문요약집
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• pp.163-163
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• 2003

• Construction Engineering and Management
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• 제14권1호
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• pp.52-57
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• 2013