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challenging research work. This paper proposes a method LET(LDA&Entropy&Tex-trank) to extract topic keywords from Sina Weibo topics text sets. LET considers both topic influence of keywords and topic discrimination of keyword that combines the merits of LDA, Entropy and TextRank. In addition, we design a new standard
system was developed by analysis of relationship among keywords of R&D classification systems in Korea. To establish an integrated classification system, network analysis was performed to define clusters consisting of keywords which have high relationship. Dataset used for analysis was project data from Governmental
Process similarity search is an effective way tomanage a large number of business process models. However,there exists no benchmark dataset that can be used to evaluate theperformance of the existing process similarity search algorithms.To solve this problem, we have constructed a benchmark datasetthat modeled by Petri-net. In this paper, the benchmark datasettotally consists of 100 process models,...
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