Looking for the Best Allocation of Scientific and Technological Resources:A Perspective of Combinatorial Optimization
As the integration of science and technology accelerates in the present era,the characteristics of their mu-tual interaction,combination,penetration,and transformation have become increasingly pronounced.In-depth exploration of the knowledge linkages between science and technology(S&T)is an essential prerequisite for accurately understanding the S&T innovation laws,promoting the transformation of scientific outcomes,and optimizing S&T innovation policies.However,there is a dearth of research that effectively captures the information from both the knowledge structure and tex-tual semantics of science and technology,let alone deeply explores the linkage from the perspective of achieving optimal matching between science and technology topics.A novel deep learning-based methodology is proposed to investigate S&T linkages,where papers and patents are ap-plied to represent science and technology.Specifically,science and technology networks are constructed based on Node2Vec and BERT.Then,science and technology topics are identified based on the Fast Unfolding algorithm and Z-Score index.Finally,a science-technology bipartite graph is constructed,the S&T topic linkages identification task is successfully transferred into a bipartite matching problem,and the maximum-weight matching is identified using a Kuhn-Munkres bipartite algorithm.Based on this,an empirical analysis is carried out using paper and patent data from the field of"Natural Language Processing"from 2010 to 2021.In validation,the proposed method is compared with four net-work construction methods in terms of topic identification,and its effectiveness is further validated against keywords linkage method and two semantic similarity methods in terms of topic similarity measurement.The results reveal that in the periods 2010-2013,2014-2017,and 2018-2021,82,51,and 91 science-technology topic pairs are identified respectively.From 2010 to 2013,interactions in the NLP field began to increase,but the depth of linkage was superficial,mainly focusing on exploring ways to improve the performance of existing models and systems.From 2014 to 2017,although the frequency of science and technology interactions slightly reduced,a more profound fu-sion of science and technology had been achieved.It is worth noting that many interactions in this period between S&T appear in discovering the role of existing scientific theories in the new technology application scenarios.From 2018 to 2021,innovation activities in the NLP field entered a vibrant phase,with both the intensity and depth of S&T linkage sig-nificantly increasing,and research and applications of multi-modality data became a new trend.The primary theoretical contributions are as follows.First,the comprehensive application of Node2Vec and BERT deep learning methods achieved the effective integration of knowledge structure and textual semantic information,deepening the application of deep learning techniques and semantic analysis methods in S&T linkage research.Second,in-novatively integrating network analysis methods,constructing the"topic coupling strength"indicator to capture the rich network structure information in scientific and technological knowledge systems,offering valuable additions to the study of S&T interactions and collaborative innovation pathways.Third,converting the science-technology topic linkage identifica-tion problem into a bipartite graph matching problem,realizing the combinatorial optimization of the S&T knowledge sys-tems,providing a fresh perspective for S&T linkage analysis.Fourth,enriching the research in fields related to industry-aca-demia-research collaborative innovation and innovation ecosystem governance,providing essential theoretical support for promoting the transformation of basic research results and driving the deep integration of innovation and industrial chains.
NETL
NSTL
万方数据
