http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 주제어 : 내용적 연관성 (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
지식 간 내용적 연관성 파악 기법의 지식 서비스 관리 접목을 위한 정량적/정성적 고려사항 검토
유기동(Keedong Yoo) 한국전자거래학회 2021 한국전자거래학회지 Vol.26 No.3
내용적 연관성에 기반한 연관지식의 파악은 핵심 지식에 대한 서비스와 보안의 기본적인 기능이다. 본 연구는 내용적 연관성을 기준으로 연관지식을 파악하는 기존의 방식, 즉 키워드 기반 방식과 워드임베딩 방식의 연관문서 네트워크 구성 성능을 비교하여 어떤 방식이 정량적/정성적 측면에서 우월한 성능을 나타내는가를 검토한다. 검토 결과 키워드 기반 방식은 핵심 문서 파악 능력과 시맨틱 정보 표현 능력 면에서 우월한 성능을, 워드임베딩 방식은 F1-Score와 Accuracy, 연관성 강도 표현 능력, 대량 문서 처리 능력 면에서 우월한 성능을 나타냈다. 본 연구의 결과는 기업과 사용자의 요구를 반영하여 보다 현실적인 연관지식 서비스 관리에 활용될 수 있다. Identification of associated knowledge based on content relevance is a fundamental functionality in managing service and security of core knowledge. This study compares the performance of methods to identify associated knowledge based on content relevance, i.e., the associated document network composition performance of keyword-based and word-embedding approach, to examine which method exhibits superior performance in terms of quantitative and qualitative perspectives. As a result, the keyword-based approach showed superior performance in core document identification and semantic information representation, while the word embedding approach showed superior performance in F1-Score and Accuracy, association intensity representation, and large-volume document processing. This study can be utilized for more realistic associated knowledge service management, reflecting the needs of companies and users.
-
신호철 ( Shin Ho-cheol ) 한말연구학회 2017 한말연구 Vol.- No.44
The purpose of this study is to seek how to distribute the standards of the two areas under the assumption that the Grammar area and the Media language area are integrated in the textbook development of `Language and media`. First, reviewing the new characteristics of the `Language and Media` in the 2015 revised curriculum by analyzing the standards and finding the distribution method of standards that is needed for developing < Language and Media >. This study suggests the partial integration method and complete integration method under the assumption that the Grammar area and the Media language area are integrated. Deviation is based on the content cohesion, topical commonness, and material commonness. This study suggests the method of deviation standards based on the fact that the main area is Grammar in `Language and Media`.
-
Keyword-based networked knowledge map expressing content relevance between knowledge
Yoo, Keedong(유기동) 한국지능정보시스템학회 2018 지능정보연구 Vol.24 No.3
A knowledge map as the taxonomy used in a knowledge repository should be structured to support and supplement knowledge activities of users who sequentially inquire and select knowledge for problem solving. The conventional knowledge map with a hierarchical structure has the advantage of systematically sorting out types and status of the knowledge to be managed, however it is not only irrelevant to knowledge user’s process of cognition and utilization, but also incapable of supporting user`s activity of querying and extracting knowledge. This study suggests a methodology for constructing a networked knowledge map that can support and reinforce the referential navigation, searching and selecting related and chained knowledge in term of contents, between knowledge. Regarding a keyword as the semantic information between knowledge, this research’s networked knowledge map can be constructed by aggregating each set of knowledge links in an automated manner. Since a keyword has the meaning of representing contents of a document, documents with common keywords have a similarity in content, and therefore the keyword-based document networks plays the role of a map expressing interactions between related knowledge. In order to examine the feasibility of the proposed methodology, 50 research papers were randomly selected, and an exemplified networked knowledge map between them with content relevance was implemented using common keywords.
ScienceON
DBpia연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
