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Knowledge Management and Data Analysis Lab teachers and students attend the international academic conference in Leiden, the Netherlands

  September 11-14, 2018, Professor Wang Xuefeng and Ph.D. students of the School of Management and Economics, Beijing Institute of Technology, Han Xiaoyu, Qiao Yali, Yang Xuemei, Li Jia, Hou Yujia, at the invitation of Professor Alan Porter of the Georgia Institute of Technology's Technology Policy Assessment Center, attended the 8th Global Tech-mining Conference and the 23rd International Conference on Science, Technology and Innovation Indicators in Leiden, the Netherlands. (The 23rd Science & Technology & Indicators).

  

  On September 11th, at the 8th Global Tech Mining Conference, Ph.D. students Han Xiaotong, Qiao Yali, master students Yang Xuemei, Li Jia, and Hou Yujia made academic reports in each of the sessions, which attracted peer attention and were recognized by the participants. Dr. Qiao Yali shared the research entitled “Identifying Potential R&D Partners: Combining Technology Complementarity and Absorptive Capacity”. Dr. Han Xiaowei made a report on "R&D Trends Analysis Based on Patent Mining: an Integrated Use of Patent Applications and Invalidation Data", using LDA theme model and two-dimensional quadrant method, comprehensively utilizing patents and invalid patents, and proposed patent-based mining. Master student Hou Yujia's academic report entitled "Technology Complementarity Measurement Based on Technology Topics" explores the measurement method of technology complementarity. Li Jia, a master student, gave a report “Technology Opportunity Analysis Combing SAO Network and Link Prediction”, innovatively combines the link prediction method with the SAO semantic network to predict the future combination of new technologies to identify new technological opportunities to provide decision-making information for technological innovation activities. Yang Xuemei shared the research of “Evaluation of Enterprise Technology Competitiveness Based on Technology Topics”, using LDA theme model to achieve topic clustering to obtain technical topics, and measuring the competitiveness of technical topics of each enterprise through relevant indicators to determine the competition of related enterprises and technical topics. 

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