• Journal of Information Management
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• v.35 no.3
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• pp.125-138
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• 2004

The purpose of this study is to propose a hierarchical user interfaces for browsing the knowledge structure of a single document. To generate the hierarchical knowledge structure, hierarchical term clustering and cluster representative term selection were performed with a single thesis in information science field, and the result was applied to design the interfaces which browse a single document hierarchically using multidimensional scaling. The interfaces can be applied to develop the user-friendly information retrieval system.

• Journal of the Korean association of regional geographers
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• v.11 no.2
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• pp.233-246
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• 2005

This paper aims to explore the innovation milieu of red-pepper agro-industry in Bonghwa County, in the northern part of Gyeongbuk Province, and suggest policy recommendations for bringing up local agro-industry from a cluster perspective. It turns out that the capabilities of Bonghwa red-pepper agro-industry to innovate are remarkably weak let alone soft infrastructures such as learning networks between innovation agents and social capital. In this sense Bonghwa red-pepper agro-industry can be viewed as an agricultural area simply specialized in production sector rather than a kind of agro-industry cluster. For moving up Bonghwa red pepper industry into an innovative cluster, the paper suggests that the policy makers need to focus on cultivating local learning communities, building a region-wide incorporated brand, promoting the local agricultural technology centre as a network hub of cluster and fostering the functional links between universities and local innovation agents, covering farmers, agricultural organizations, agro-food producers and the local government.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.51-55
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• 2016

본 논문에서는 임상 의사 결정 지원을 위한 UMLS와 위키피디아를 이용하여 지식 정보를 추출하고 질병중심 문서 클러스터와 단어 의미 표현을 이용하여 질의 확장 및 문서를 재순위화하는 방법을 제안한다. 질의로는 해당 환자가 겪고 있는 증상들이 주어진다. UMLS와 위키피디아를 사용하여 병명과 병과 관련된 증상, 검사 방법, 치료 방법 정보를 추출하고 의학 인과 관계를 구축한다. 또한, 위키피디아에 나타나는 의학 용어들에 대하여 단어의 효율적인 의미 추정 기법을 이용하여 질병 어휘의 의미 표현 벡터를 구축하고 임상 인과 관계를 이용하여 질병 중심 문서 클러스터를 구축한다. 추출한 의학 정보를 이용하여 질의와 관련된 병명을 추출한다. 이후 질의와 관련된 병명과 단어 의미 표현을 이용하여 확장 질의를 선택한다. 또한, 질병 중심 문서 클러스터를 이용하여 문서 재순위화를 진행한다. 제안 방법의 유효성을 검증하기 위해 TREC Clinical Decision Support(CDS) 2014, 2015 테스트 컬렉션에 대해 비교 평가한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.523-528
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• 2015

In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.

• 한국벤처창업학회:학술대회논문집
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• 2022.04a
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• pp.73-79
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• 2022

본 연구는 동시출현단어 분석과 토픽모델링을 통해 기업가정신의 연구주제와 연구 동향을 분석하여 기업가정신 연구에 대한 향후 연구방향을 수립하기 위한 정보를 제공하는 것이 목적이다. 이를 위해 Web of Science 데이터베이스에서 "entrepreneurship"을 기본검색어로 설정하고, 2002년부터 2021년까지 발표한 영어 논문으로 제한하여 기업가정신 논문의 데이터를 다운로드하여 데이터를 확보하였다. 본 연구에서는 VOSviewer 프로그램을 이용하여 동시출현단어 분석을 하였고, R 프로그램을 이용하여 토픽모델링 분석을 하였다. 동시출현단어 분석 결과, 기업가정신과 혁신 클러스터, 기업가정신 교육 클러스터, 사회적 기업가정신과 지속가능성 클러스터, 기업성과 클러스터, 그리고 지식 및 기술이전 클러스터 등 5개의 클러스터로 구분되었다. 토픽모델링 분석 결과, 창업환경 및 경제발전, 국제 기업가정신, 다양한 기업가정신, 벤처기업과 자본조달, 정부정책 및 지원, 사회적 기업가정신, 경영관련 이슈, 지역도시계획 및 개발, 기업가정신 교육, 기업가의 혁신과 성과, 기업가정신 연구, 기업가의 창업의도 등 12개의 토픽으로 분석되었다. 본 연구의 결과는 기업가정신 연구에 대한 전반적인 연구동향을 파악할 뿐만 아니라, 기업가정신과 관련된 어떠한 연구 주제들이 다루어져 왔는지에 대해 분석함으로써 기업가정신에 대한 연구의 이해도를 높이고 기업가정신 연구가 가져올 방향성을 제안하는데 활용할 수 있을 것으로 기대된다.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.2
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• pp.249-263
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• 2014

Established as the first science park in the world in the late 1950's, the Research Triangle Park(RTP) has not jut grown significantly but also has been successful in the transition from the exogenous development model to the endogenous development model. In this context, this paper attempts to explore the evolutionary path of the RTP by drawing upon the concept of triple-helix spaces of regional innovation. Firstly, the three research universities in the triangle area, as a knowledge space, played a fundamental role for forming the RTP. However, it is difficult to say that the regional universities, as opposed to the Silicon Valley and the Boston area, have had a significant impact on inducing the dynamics of the cluster evolution and the triple helix spaces. Secondly, it can be argued that the North Carolina's Board of Science and Technology, which was formed in 1961 but traced back to the 1950's in its origin, has been a centerpiece of a consensus space that makes a contribution to creating, sustaining and transforming the RTP as a triple-helix-based innovation cluster. Thirdly, there have been a plenty of agents to be an innovation space in the RTP. Particularly, the North Carolina Biotechnology Center(NCBC) and the Microelectronic Center of North Carolina(MCNC) have been the boundary permeable agents to make triple-helix agents interact. Today, the RTP has the triple-helix spaces with the structure that a consensus spaces is centered on out of the three, but all of those are inter-connected and influenced by each other. It can be claimed that the RTP today shows the dynamic structure of cluster evolution in a way in which the existing industry sectors have adapted to the changes in external environment and the new industry sectors have emerged at the same time.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Journal of the Economic Geographical Society of Korea
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• v.8 no.2
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• pp.195-215
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• 2005

This study was carried into execution to analyze Spatial Characteristics of Seoul Business Services Cluster in circumstance of local economic development led by knowledge-intensive business services. The analysis was performed for industrial agglomeration and companies' linkage and network. As the result, there are three business services cluster, including the largest one in Gang-Nam Gu, Seoul, and for last 10 years, there has been increasing development of business services cluster. In the meanwhile, their linkage and network have not been performed briskly. Therefore, from now on, a plan that can help companies' linkage and network performed inside of cluster to have active and international structure has to be considered in cluster policy.

• Journal of the Korean association of regional geographers
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• v.22 no.2
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• pp.397-410
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• 2016

Although industrial complexes have played as an engine of the Korean economy for the last 40 years, the majority of industrial complexes shows limitations to the continuous growth such as a lack of innovation capabilities and social capital, conceived as a key to transforming into clusters of innovation. To overcome those problems, the Korean government embarked on the cluster policy from the mid 2000's, focusing on promoting the endogenous development capabilities of individual industrial complexes. Drawing upon the in-depth case study of the Gumi IT cluster, one of the representative large-scale industrial complexes in Korea, the authors conclude that the cluster policy has contributed to making the Gumi IT cluster enhance the capabilities of endogenous development through the facilitation of self-organizing learning communities within the cluster.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1316-1319
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• 2015

본 논문에서는 대규모 분산 병렬 컴퓨팅 환경인 하둡 클러스터 시스템을 이용하여, 공간 객체들 간의 위상 관계를 효율적으로 추론하는 대용량 정성 공간 추론기를 제안한다. 본 논문에서 제안하는 공간 추론기는 추론 작업의 순차성과 반복성을 고려하여, 작업들 간의 디스크 입출력을 최소화할 수 있는 인-메모리 기반의 아파치 스파크 프레임워크를 이용하여 개발하였다. 따라서 본 추론기에서는 추론의 대상이 되는 대용량 공간 지식들을 아파치 스파크의 분산 데이터 집합 형태인 PairRDD와 RDD로 변환하고, 이들에 대한 데이터 오퍼레이션들로 추론 작업들을 구현하였다. 또한, 본 추론기에서는 추론 시간의 많은 부분을 차지하는 이행 관계 추론에 필요한 조합표를 효과적으로 축소함으로써, 공간 추론 작업의 성능을 크게 향상시켰다. 대용량의 공간 지식 베이스를 이용한 성능 분석 실험을 통해, 본 논문에서 제안한 정성 공간 추론기의 높은 성능을 확인할 수 있었다.