Evolution Path Identification and Visualization of Technological Innovation Based on SAO
Liu Chunjiang1, Liu Ziqiang2, Fang Shu1
1.Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041 2.School of Journalism and Communication, Nanjing Normal University, Nanjing 210097
摘要利用专利文献数据识别技术领域的技术主题演化发展路径并分析其发展趋势,对于科技界、企业界进行专利技术创新具有重要的意义。首先,使用Open IE 5.1进行SAO(subject-action-object)三元组抽取,基于LDA(latent Dirichlet allocation)模型进行主题识别,根据TRIZ技术创新思想,基于action语义词典将技术主题划分到四个维度;然后,通过计算SAO三元组之间的相似度来测度技术主题之间的语义关联构建技术主题创新演化路径,并利用可视化技术构建技术主题创新演化路径可视化图谱,利用该图谱辅助分析技术主题演化脉络及其发展趋势。最后,通过石墨烯超级电容器(集流体)领域的实证,对该领域的技术问题(problem to problem,P-P)主题、技术功能(solution to solution,S-S)主题、解决方案(problem to solution,P-S)主题和技术效果(solution to problem,S-P)主题的创新演化路径进行解读分析,验证了本研究提出方法的可行性和有效性。
刘春江, 刘自强, 方曙. 基于SAO的技术主题创新演化路径识别及其可视化研究[J]. 情报学报, 2023, 42(2): 164-175.
Liu Chunjiang, Liu Ziqiang, Fang Shu. Evolution Path Identification and Visualization of Technological Innovation Based on SAO. 情报学报, 2023, 42(2): 164-175.
1 国务院关于印发“十三五”国家科技创新规划的通知[EB/OL]. (2016-08-10) [2020-06-02]. https://www.most.gov.cn/xxgk/xinxifenlei/fdzdgknr/gjkjgh/201608/t20160810_127174.html. 2 刘自强, 许海云, 岳丽欣, 等. 基于Chunk-LDAvis的核心技术主题识别方法研究[J]. 图书情报工作, 2019, 63(9): 73-84. 3 Cho Y, Kim M. Entropy and gravity concepts as new methodological indexes to investigate technological convergence: patent network-based approach[J]. PLoS One, 2014, 9(6): e98009. 4 Rosenberg N. Exploring the black box: technology, economics, and history[M]. Cambridge: Cambridge University Press, 1994. 5 杨博, 蔡东风, 杨华. 开放式信息抽取研究进展[J]. 中文信息学报, 2014, 28(4): 1-11, 36. 6 杨超, 朱东华, 衡晓帆, 等. 基于语法树的SAO结构识别方法研究[J]. 图书情报工作, 2016, 60(21): 113-121. 7 张晗, 赵玉虹. 基于语义图的医学多文档摘要提取模型构建[J]. 图书情报工作, 2017, 61(8): 112-119. 8 Ahlers C B, Fiszman M, Demner-Fushman D, et al. Extracting semantic predications from Medline citations for pharmacogenomics[J]. Pacific Symposium on Biocomputing, 2007, 12: 209-220. 9 Park H, Yoon J, Kim K. Identifying patent infringement using SAO based semantic technological similarities[J]. Scientometrics, 2012, 90(2): 515-529. 10 Choi S, Park H, Kang D, et al. An SAO-based text mining approach to building a technology tree for technology planning[J]. Expert Systems with Applications, 2012, 39(13): 11443-11455. 11 段庆锋, 蒋保建. 基于SAO结构的专利技术功效图构建研究[J]. 现代情报, 2017, 37(6): 48-54. 12 马晨浩. 基于甲状腺知识图谱的自动问答系统设计与实现[D]. 上海: 东华大学, 2018. 13 张玉洁, 白如江, 刘明月, 等. 融合语义联想和BERT的图情领域SAO短文本分类研究[J]. 图书情报工作, 2021, 65(16): 118-129. 14 周海炜, 吴成凤. 基于专利SAO结构和多指标评价的新兴技术识别研究——以手机芯片领域为例[J]. 情报杂志, 2022, 41(2): 86-94, 48. 15 Choi C, Park Y. Monitoring the organic structure of technology based on the patent development paths[J]. Technological Forecasting and Social Change, 2009, 76(6): 754-768. 16 Wissema J G. Morphological analysis: its application to a company TF investigation[J]. Futures, 1976, 8(2): 146-153. 17 Ilevbare I M, Probert D, Phaal R. A review of TRIZ, and its benefits and challenges in practice[J]. Technovation, 2013, 33(2/3): 30-37. 18 Mitchell V W. The Delphi technique: an exposition and application[J]. Technology Analysis & Strategic Management, 1991, 3(4): 333-358. 19 Verspagen B. Mapping technological trajectories as patent citation networks: a study on the history of fuel cell research[J]. Advances in Complex Systems, 2007, 10(1): 93-115. 20 Martinelli A. An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry[J]. Research Policy, 2012, 41(2): 414-429. 21 James T L, Cook D F, Conlon S, et al. A framework to explore innovation at SAP through bibliometric analysis of patent applications[J]. Expert Systems with Applications, 2015, 42(24): 9389-9401. 22 刘小玲, 谭宗颖. 基于专利网络的技术演进研究方法探索[J]. 科学学研究, 2013, 31(5): 651-656, 731. 23 陈伟, 林超然, 李金秋, 等. 基于LDA-HMM的专利技术主题演化趋势分析——以船用柴油机技术为例[J]. 情报学报, 2018, 37(7): 732-741. 24 杨超, 朱东华, 汪雪锋, 等. 专利技术主题分析: 基于SAO结构的LDA主题模型方法[J]. 图书情报工作, 2017, 61(3): 86-96. 25 李欣, 王静静, 杨梓, 等. 基于SAO结构语义分析的新兴技术识别研究[J]. 情报杂志, 2016, 35(3): 80-84. 26 李欣, 谢前前, 黄鲁成, 等. 基于SAO结构语义挖掘的新兴技术演化轨迹研究[J]. 科学学与科学技术管理, 2018, 39(1): 17-31. 27 冯立杰, 曾小红, 王金凤, 等. 一种三级技术机会识别方法及其应用——基于SAO语义分析和多维技术创新地图[J]. 科技进步与对策, 2021, 38(19): 1-10. 28 Han X T, Zhu D H, Wang X F, et al. Technology opportunity analysis: combining SAO networks and link prediction[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1288-1298. 29 胡正银, 刘春江. 基于语义TRIZ的专利技术挖掘[M]. 北京: 科学出版社, 2021. 30 Saha S, Mausam. Open information extraction from conjunctive sentences[C]// Proceedings of the 27th International Conference on Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 2288-2299. 31 Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022. 32 Blei D M, Lafferty J D. Dynamic topic models[C]// Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 113-120. 33 Landauer T K, Dumais S T. A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge[J]. Psychological Review, 1997, 104(2): 211-240. 34 Shen C, Li T, Ding C. Integrating clustering and multi-document summarization by bi-mixture probabilistic latent semantic analysis (PLSA) with sentence bases[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2011, 25(1): 914-920. 35 刘自强, 许海云, 岳丽欣, 等. 面向研究前沿预测的主题扩散演化滞后效应研究[J]. 情报学报, 2018, 37(10): 979-988. 36 任晓亚, 张志强, 陈云伟. 杰出科学家的科研产出规律——以拉斯克医学研究奖得主为例[J]. 情报学报, 2019, 38(9): 894-906. 37 Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[OL]. (2013-09-07). https://arxiv.org/pdf/1301.3781.pdf. 38 Wang X F, Zhang S, Liu Y Q. ITGInsight-discovering and visualizing research fronts in the scientific literature[J]. Scientometrics, 2022, 127: 6509-6531. 39 王晓光, 程齐凯. 基于NEViewer的学科主题演化可视化分析[J]. 情报学报, 2013, 32(9): 900-911.