Integrative model for discovering linked topics in science and technology |
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| Institution: | 1. Business School, Shandong University of Technology, Zibo, Shandong 255049, China;2. School of Medical Information Engineering, Jining Medical University, Rizhao, Shandong 276826, China;3. Library, Shenzhen University, Shenzhen, Guangdong 518060, China;4. School of Economics, Shandong University of Technology, Zibo, Shandong 255049, China;5. Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China;1. Warsaw University of Technology, Faculty of Mathematics and Information Science, ul. Koszykowa 75, Warsaw 00-662, Poland;2. Deakin University, School of IT, Geelong, VIC 3220, Australia;3. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, Warsaw 01-447, Poland;4. Warsaw University of Technology, Faculty of Physics, ul. Koszykowa 75, Warsaw 00-662, Poland;1. Laboratory of Digital and Computational Demography, Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany;2. German Centre for Higher Education Research and Science Studies (DZHW), Berlin, Germany;3. Department of Social and Political Sciences, University of Milan, Milan, Italy;1. Department of Statistics, Cochin University of Science and Technology, Cochin 682022, India;2. Department of Statistics, Government Arts College, Thiruvananthapuram 695014, India;1. Department of Library and Information Science Education, College of Education, Kongju National University, Gongju 32588, Republic of Korea;2. Department of Library and Information Science, Hannam University, Daejeon 34430, Republic of Korea;3. Department of Library and Information Science, Cheongju University, Cheongju 28503, Republic of Korea;4. Department of Library and Information Science, Yonsei University, Seoul 03722, Republic of Korea |
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| Abstract: | Linked topics in science and technology (LTSTs) can provide new avenues for technological innovation and are a key step in the transition from basic to applied research. This paper proposes a science and technology semantic linkage integration model for discovering LTSTs. Particularly, the integrative model fuses the term co-occurrence networks of basic and applied research, which expands the completeness of topic networks by enhancing the semantic characteristics of these networks. It is found that link prediction can further reinforce the semantic association of topic terms in networks between basic and applied topics. Simple fusion explicitly linked the topic terms, which can be used as automatic seed marking for subsequent link prediction to identify implicit linking of topic terms. Furthermore, an application to the gene-engineered vaccines field depicted that newly predicted implicit relations can effectively identify LTSTs. The results also show that implicit semantic recognition of LTSTs can be enhanced through simple fusion, while the recognition of LTST can be improved through link prediction. Therefore, the proposed model can assist experts to identify LTSTs that cannot be recognized through simple fusion. |
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