• Title/Summary/Keyword: Weighted Formal Concept Analysis

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공동 출원 특허정보와 정형적 개념분석을 활용한 기술협력 성과 분석 연구 (Exploring Technology Cooperation Performance Using co-patent Information and Formal Concept Analysis)

  • 박찬호;이희정
    • 산업경영시스템학회지
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    • 제46권spc호
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    • pp.39-53
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    • 2023
  • Recently, the market competition has been fiercer due to the acceleration of technological change and the launch of intelligent products. In this situation, technology cooperation activities through networks rather than independent technological innovation activities of a single company or institution are recognized as a crucial strategy to gain competitiveness. Technology cooperation can take various forms depending on the target technology, and researchers have conducted performance analyses of technology cooperation types. However, there have not been data-based quantitative studies on the types and trends of technology cooperation for the target technology. In this paper, we explored the difference between the technology cooperation types by technology and time using the formal concept analysis method and co-patent information. In particular, the proposed methodology has been verified through the case study of electric vehicles, and it is intended to suggest the direction of technological cooperation according to specific technologies and cooperation targets in the future

Computing Semantic Similarity between ECG-Information Concepts Based on an Entropy-Weighted Concept Lattice

  • Wang, Kai;Yang, Shu
    • Journal of Information Processing Systems
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    • 제16권1호
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    • pp.184-200
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    • 2020
  • Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.