[1]JIN Chen,XIE Zhenping,REN Liyuan,et al.Evolutionary path mining of domain knowledge by joint modeling in space-time domain[J].CAAI Transactions on Intelligent Systems,2017,12(5):735-744.[doi:10.11992/tis.201706023]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 5
Page number:
735-744
Column:
学术论文—人工智能基础
Public date:
2017-10-25
- Title:
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Evolutionary path mining of domain knowledge by joint modeling in space-time domain
- Author(s):
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JIN Chen; XIE Zhenping; REN Liyuan; LIU Yuan
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1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi 214122, China
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- Keywords:
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knowledge evolution; evolution path; knowledge network; knowledge systems; space-time domain combination; skeleton clustering; digital media knowledge
- CLC:
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TP181
- DOI:
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10.11992/tis.201706023
- Abstract:
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In special technology fields, there might be evolutionary relationships between various knowledge concepts, and these evolutionary relationship can be used to depict the developmental venation of the corresponding technology field. However, the characteristics of fragmentation, disorder, and large scale in domain knowledge systems make it difficult for users to accurately identify these knowledge relationships. To address this problem, in this paper, we propose an evolutionary path mining method based on skeleton clustering and the joint modeling of domain knowledge with respect to the space-time correlation. In this method, first we express the knowledge system as a knowledge network with joint space-time correlations, then we adopt the skeleton clustering method to extract the evolutionary path of the knowledge network. In addition, we analyze the connection between the evolutionary paths based on the development of the knowledge concept. An experimental analysis of the digital media domain shows that the proposed method can effectively extract the evolutionary path of domain knowledge, which has significant value for knowledge learning and personalized recommendation.