• Title/Summary/Keyword: 온톨러지

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Category Reorganization for Ontology Reuse (온톨러지 재사용을 위한 범주 재분류)

  • Yang Jae-Gun;Lee Jong-Hyeok;Bae Jae-Hak J.;Bae Jae-Hak J.
    • The KIPS Transactions:PartB
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    • v.12B no.1 s.97
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    • pp.69-80
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    • 2005
  • This paper introduces a methodology of transforming an existing ontology into the one that satisfies its application. The transformation consists of simplification and realization of word category information. They are based on category headings and base categories. Furthermore, this paper describes a method by which we can identify relationships between category sets. Through the transformation, (1) Roget's thesaurus is reorganized into 7 categories and the base of 'Ontology for Narrative'[32], (2) 22 immersion factors of multimedia games can be subdivided into 207 factors in [35], and (3) the relationships between 10 mental factors and 22 immersion factors of multimedia games are identified in [36].

Improvement of a Sentence Analysis System through Lexical Expansion (어휘확장을 통한 문장분석 시스템의 개선)

  • Kim Min-Chan;Kim Gon;Bae Jae-Hak
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.07b
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    • pp.496-498
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    • 2005
  • 본 논문에서는 미등록 어휘로 인한 구문분석의 실패를 해결하는 방법으로 WordNet의 유의어 정보를 이용하였다. 이 방법을 또한 설화용 온톨러지 OfN의 어휘확장에 적용하였다. 실험을 통하여 구문분석 과정에서 나타나는 미등록 어휘문제의 해결과 문장의 의미분석 과정이 순조롭게 진행될 수 있음을 확인하였다.

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Semi-automatic Ontology construction based on Hub word (허브 단어에 기반한 온톨러지의 반자동 구축)

  • 임수연;구상옥;송무희;이상조
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.04c
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    • pp.377-379
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    • 2003
  • 본 논문은 문서검 색을 위한 온톨러지(Ontology)의 반자동 구축방안을 제시한다. 이를 위하여 우리는 다른 단어들과 특히 많은 관련이 있는 단어를 허브 단어(hub word)라고 정의하며 경제분야에 특정적인 온톨러지의 구축을 위하여 TREC 문서집합의 Wall Street Journal 문서들을 분석하였다. 문서집합 내의 모든 단어들의 tf, idf 값를 이용하여 허브 단어를 결정짓고 이렇게 선택된 허브 단어들을 중심으로 온톨러지를 구축하였다. 우리는 허브 단어와 다른 단어들간의 관계를 문서로부터 자동으로 추출하고 그 정보를 이용하여 온톨러지를 확장해나간다. 제안된 온톨러지는 전통적인 문서 검색의 인덱스 파일과 같은 역할을 하게 되며, 간단한 역파일(inverted file) 구조보다 더 많은 의미정보(semantic information)를 제공할 수 있다.

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Immersion Power Decomposition of Multimedia Games with Ontology (온톨러지를 활용한 멀티미디어 게임 몰입력 분해)

  • Yang Jae-Gun;Bae Jae-Hak;Lee Jong-Hyeok
    • Korean Journal of Cognitive Science
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    • v.15 no.3
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    • pp.45-55
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    • 2004
  • Immersion means the state of total devotion to what one is involved in. The typical form of this immersion experience happens when one is engaging in computer games. We analyze how the users became immersed in computer games. There are 22 elements of immersion involved in cognitive and emotional fun based cm the flow theory. After analyzing these elements with respect to flow dimension and game mechanics, we find that there are differences in the degree of their immersion in games. There are 7 elements that may be considered preferentially in designing computer games: unity, stimulation, gambling, virtual reality, fantasy, satisfaction accomplishment. We match these elements to Roget's categories and search for new categories with heading information and reference information. As a result 22 immersion elements are subdivided into 229 factors in the concrete. With these factors, we can design a multimedia game which has mon powerful immersion.

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Sentence ion : Sentence Revision with Concept ion (문장추상화 : 개념추상화를 도입한 문장교열)

  • Kim, Gon;Yang, Jaegun;Bae, Jaehak;Lee, Jonghyuk
    • The KIPS Transactions:PartB
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    • v.11B no.5
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    • pp.563-572
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    • 2004
  • Sentence ion is a simplification of a sentence preserving its communicative function. It accomplishes sentence revision and concept ion simultaneously. Sentence revision is a method that resolves the discrepancy between human's thoughts and its expressed semantic in sentences. Concept ion is an expression of general ideas acquired from the common elements of concepts. Sentence ion selects the main constituents of given sentences and describes the upper concepts of them with detecting their semantic information. This enables sen fence revision and concept ion simultaneously. In this paper, a syntactic parser LGPI+ and an ontology OfN are utilized for sentence ion. Sentence abstracter SABOT makes use of LGPI+ and OfN. SABOT processes the result of parsing and selects the candidate words for sentence ion. This paper computes the sentence recall of the main sentences and the topic hit ratio of the selected sentences with the text understanding system using sentence ion. The sources are 58 paragraphs in 23 stories. As a result of it, the sentence recall is about .54 ~ 72% and the topic hit ratio is about 76 ~ 86%. This paper verified that sentence ion enables sentence revision that can select the topic sentences of a given text efficiently and concept ion that can improve the depth of text understanding.