Hong Gee Kim

Seoul National University, Sŏul, Seoul, South Korea

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Publications (5)0.29 Total impact

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    ABSTRACT: This study was done to explore the knowledge structure of Korean Nursing Science. The main variables were key words from the research papers that were presented in the Journal of Korean Academy of Nursing and journals of the seven branches of the Korean Academy of Nursing. English titles and abstracts of the papers (n=5,936) published from 1995 through 2009 were included. Noun phrases were extracted from the corpora using an in-house program (BiKE Text Analyzer), and their co-occurrence networks were generated via a cosine similarity measure, and then the networks were analyzed and visualized using Pajek, a Social Network Analysis program. With the hub and authority measures, the most important research topics in Korean Nursing Science were identified. Newly emerging topics by three-year period units were observed as research trends. This study provides a systematic overview on the knowledge structure of Korean Nursing Science. The Social Network Analysis for this study will be useful for identifying the knowledge structure in Nursing Science.
    Journal of Korean Academy of Nursing 10/2011; 41(5):623-32. · 0.29 Impact Factor
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    ABSTRACT: There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.
    01/2008;
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    ABSTRACT: We describe an open tagging platform that aims to make tagging data open, more universal, and apply it across different tagging sites. We implement the web-based application -int.ere.st -to realize this goal. int.ere.st collects in-formation related to tagging behaviors from Web 2.0 sites and offer direct access using Semantic Web technologies such as Linked Data. The application is avail-able at http://int.ere.st/.
    01/2008;
  • Hak Lae Kim, Hong Gee Kim, S. Decker
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    ABSTRACT: In this paper we discuss the need to interact both desktop and Web environments for making semantic documentation and the advantages of using semantic Web technologies and social Web Services. We propose the architecture and then describe the prototype system based on the proposed architecture
    Next Generation Web Services Practices, 2006. NWeSP 2006. International Conference on; 10/2006
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    Hong Gee Kim, Jae Hwa Choi, Stefan Decker
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    ABSTRACT: Managing metadata of documents is a difficult and slippery for desktop users. A wide variety of technologies have been applied for supporting requirements of metadata management, ranging from the acquisition, creation, maintenance, retrieval, reuse, and publishing of metadata. We introduce essential concepts of a semantic document and implement the necessary functionality of metadata managing process. We also propose that three tasks are required to facilitate unambiguous representation of metadata in documents: using XMP to store metadata with the file itself, using ontologies to represent semantic concepts and using Social Web services to interact with web based resources. So our approach allows a user to interact and share the resources among a Desktop and Web more easily.