基于主题模型的微藻生物燃料产业链专利技术分析<sup>*</sup>

doi:10.11925/infotech.2096-3467.2017.1319

摘要
参考文献
相关文章
Metrics

全文: PDF(1498 KB) HTML ( 5 )
输出: BibTeX | EndNote (RIS)

摘要

【目的】基于主题模型挖掘微藻生物燃料产业链技术及技术继承关系。【方法】构建产业链模型, 基于改进的LDA方法实现产业链环节-技术主题-专利映射; 统计研发主体, 分析技术发展趋势; 构建基于语义相似度的专利加权引文网络, 绘制产业链专利发展地图。【结果】在算法方面, 基于短语抽取规则的LDA方法能够实现更精确的技术主题识别; 在分析结果方面, 得出微藻生物燃料产业链技术发展趋势, 揭示产业链环节技术继承关系。【局限】主要针对微藻生物燃料产业链进行研究, 建模方法若推广应用于其他产业, 需要具有一定的目标产业背景知识。【结论】有效识别了微藻生物燃料产业链重点及热点环节, 该产业链技术创新需多环节协同。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张杰
	赵君博
	翟东升
	孙宁宁

关键词 ：微藻生物燃料, 产业链, LDA主题模型, 专利

Abstract：

[Objective] This paper analyzes microalgae biofuel industrial chain technology and the technology inheritance based on topic model, aiming at promoting technological innovations of this industry in China. [Methods] Firstly, we construct the microalgae biofuel industrial chain model, and build the mapping relationship between the industrial chain, technical topics and patents based on the improved LDA topic method. Then, we discover the R&D subjects and analyze technology development trend. Finally, to draw the patent development map under industrial chain segments, the patent-weighted citation network based on semantic similarity is constructed. [Results] In the aspect of algorithm, this paper achieves more accurate topic identification by the improved LDA method. It also find out the development trend of the microalgae biofuel industrial chain technology, and the technical inheritance of industrial chain segments. [Limitations] This paper only focus on the microalgae biofuel industrial chain technology, and a certain degree of background knowledge on the object industry for researchers is necessary when these models as well as results are applied to other industries. [Conclusions] It identifies the key technical segments and hot spots of microalgae biofuel industry chain, and shows that the achievement of technological innovations in this field needs the coordination of more than one segments.

Key words： Microalgae Biofuel Industrial Chain LDA Topic Model Patent

收稿日期: 2017-12-25

基金资助:*本文系教育部人文社会科学青年项目“核型结构产业集群多网络建模及应用研究”(项目编号: 14YJC630035)、国家自然科学基金青年项目“政府项目式驱动创新行为的企业响应机制研究: 复杂适应系统视角”(项目编号: 71503011)和广东省科技计划项目“基于专利语义分析的技术合作伙伴推荐服务平台”(项目编号: 2017A040403027)的研究成果之一

引用本文:

张杰,赵君博,翟东升,孙宁宁. 基于主题模型的微藻生物燃料产业链专利技术分析^*[J]. 数据分析与知识发现, 2019, 3(2): 52-64.
Jie Zhang,Junbo Zhao,Dongsheng Zhai,Ningning Sun. Patent Technology Analysis of Microalgae Biofuel Industrial Chain Based on Topic Model. Data Analysis and Knowledge Discovery, DOI：10.11925/infotech.2096-3467.2017.1319.

链接本文:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.1319

[1]	Kim G, Park S, Jang D.Technology Analysis from Patent Data Using Latent Dirichlet Allocation[A]// Lee K M, Park S J, Lee J H. Soft Computing in Big Data Processing[M]. Springer International Publishing, 2014: 71-80.
[2]	Venugopalan S, Rai V.Topic Based Classification and Pattern Identification in Patents[J]. Technological Forecasting & Social Change, 2015, 94: 236-250.
[3]	廖列法, 勒孚刚, 朱亚兰. LDA模型在专利文本分类中的应用[J]. 现代情报, 2017, 37(3): 35-39.
[3]	(Liao Liefa, Le Fugang, Zhu Yalan.The Application of LDA Model in Patent Text Classification[J]. Journal of Modern Information, 2017, 37(3): 35-39.)
[4]	Chen H, Zhang Y, Zhu D.Identifying Technological Topic Changes in Patent Claims Using Topic Modeling[A]// Daim T U, Chiavetta D, Porter A L, et al. Anticipating Future Innovation Pathways Through Large Data Analysis[M]. Springer International Publishing, 2016: 187-209.
[5]	Kaplan S, Vakili K, Novelty V S.Usefulness in Innovative Breakthroughs: A Test Using Topic Modeling of Nanotechnology Patents[J]. Strategic Management Journal, 2015, 36: 1435-1457.
[6]	贾龙飞. 专利文献主题发现方法的比较研究——以汽车零部件领域为例[D]. 大连: 大连理工大学, 2014.
[6]	(Jia Longfei.Comparative Research of Topic Discovery Methods in Patents Documents——Study on the Case of Auto Parts[D]. Dalian: Dalian University of Technology, 2014.)
[7]	郑学益. 构筑产业链形成核心竞争力—兼谈福建发展的定位及其战略选择[J]. 开放潮, 2000(8): 14-15.
[7]	(Zheng Xueyi. Constructing Industry Chains, Forming Core Competence - and Discussing the Positioning of Fujian’s Development and Its Strategic Choice[J]. Open at Tide, 2000(8): 14-15.)
[8]	刘立, 王博. 基于专利情报分析的数控机床产业研究[J]. 科技管理研究, 2010, 30(15): 149-152.
[8]	(Liu Li, Wang Bo.Analysis of CNC Machine Industry Based on Patent Intelligence[J]. Science and Technology Management Research, 2010, 30(15): 149-152.)
[9]	吴菲菲, 张亚茹, 黄鲁成, 等. 基于AToT模型的技术主题多维动态演化分析——以石墨烯技术为例[J]. 图书情报工作, 2017, 61(5): 95-102.
[9]	(Wu Feifei, Zhang Yaru, Huang Lucheng, et al.Multi-dimension Dynamic Evolution Analysis of Technology Topics Based on AToT by Taking Grapheme Technology as an Example[J]. Library and Information Service. 2017, 61(5): 95-102.)
[10]	陈芳, 郑菲, 彭皓, 等. 基于产业链的产业技术分析方法研究——以食品产业技术分析为例[J]. 图书情报工作, 2013, 57(18): 31-37.
[10]	(Chen Fang, Zheng Fei, Peng Hao, et al.Research on Industry and Technology Analysis Method Based on Industry Chain Mapping: Taking Food Industry Analysis for Example[J]. Library and Information Service, 2013, 57(18): 31-37.)
[11]	Conte M, Iacobazzi A, Ronchetti M, et al.Hydrogen Economy for a Sustainable Development: State-of-the-Art and Technological Perspectives[J]. Journal of Power Sources, 2001, 100(1): 171-187.
[12]	Yasunaga Y, Watanabe M, Korenaga M.Application of Technology Roadmaps to Governmental Innovation Policy for Promoting Technology Convergence[J]. Technological Forecasting and Social Change, 2009, 76(1): 61-79.
[13]	Mata T M, Martins A A, Caetano N S.Microalgae for Biodiesel Production and Other Applications: A Review[J]. Renewable and Sustainable Energy Reviews, 2010, 14(1): 217-232.
[14]	Singh J, Gu S.Commercialization Potential of Microalgae for Biofuels Production[J]. Renewable and Sustainable Energy Reviews, 2010, 14(9): 2596-2610.
[15]	Kumar S P J, Kumar G V, Dash A, et al. Sustainable Green Solvents and Techniques for Lipid Extraction from Microalgae: A Review[J]. Algal Research, 2017, 21: 138-147.
[16]	刘雪艳, 苏忠亮. 微藻生物燃料的研究进展[J]. 化学与生物工程, 2017, 34(3): 11-14.
[16]	(Liu Xueyan, Su Zhongliang.Research Progress on Microalgae Biofuel[J]. Chemistry & Bioengineering, 2017, 34(3): 11-14.)
[17]	王博, 刘盛博, 丁堃, 等. 基于LDA主题模型的专利内容分析方法[J]. 科研管理, 2015(3): 111-117.
[17]	(Wang Bo, Liu Shengbo, Ding Kun, et al.Patent Content Analysis Method Based on LDA Topic Model[J]. Science Research Management, 2015(3): 111-117.)
[18]	Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[19]	Newman M.Networks: An Introduction[M]. New York: Oxford University Press, 2010: 345-382.
[20]	Hofmann M, Chisholm A.Text Mining and Visualization: Case Studies Using Open-source Tools[M]. CRC Press, 2016: 241-264.
[21]	Deza E, Deza M M.Dictionary of Distances[M]. Elsevier Science, 2006: 288-296.

[1]	周成,魏红芹. *专利价值评估与分类研究^——基于自组织映射支持向量机**[J]. 数据分析与知识发现, 2019, 3(5): 117-124.
[2]	张金柱,胡一鸣. 融合表示学习与机器学习的专利科学引文标题自动抽取研究^*[J]. 数据分析与知识发现, 2019, 3(5): 68-76.
[3]	席林娜,窦永香. 基于计划行为理论的微博用户转发行为影响因素研究^*[J]. 数据分析与知识发现, 2019, 3(2): 13-20.
[4]	刘俊婉,龙志昕,王菲菲. 基于LDA主题模型与链路预测的新兴主题关联机会发现研究^*[J]. 数据分析与知识发现, 2019, 3(1): 104-117.
[5]	杨贵军,徐雪,赵富强. 基于XGBoost算法的用户评分预测模型及应用^*[J]. 数据分析与知识发现, 2019, 3(1): 118-126.
[6]	王雪颖,王昊,张紫玄. 中文专利文献中连续符号串的语义识别^*[J]. 数据分析与知识发现, 2018, 2(5): 11-22.
[7]	俞琰,赵乃瑄. 加权专利文本主题模型研究^*[J]. 数据分析与知识发现, 2018, 2(4): 81-89.
[8]	王丽,邹丽雪,刘细文. 基于LDA主题模型的文献关联分析及可视化研究[J]. 数据分析与知识发现, 2018, 2(3): 98-106.
[9]	李贺,祝琳琳,闫敏,刘金承,洪闯. 开放式创新社区用户信息有用性识别研究^*[J]. 数据分析与知识发现, 2018, 2(12): 12-22.
[10]	俞琰,赵乃瑄. 基于辅助集的专利主题分析领域停用词选取^*[J]. 数据分析与知识发现, 2018, 2(11): 95-103.
[11]	曲佳彬,欧石燕. 基于主题过滤与主题关联的学科主题演化分析^*[J]. 数据分析与知识发现, 2018, 2(1): 64-75.
[12]	贾杉杉,刘畅,孙连英,刘小安,彭涛. 基于多特征多分类器集成的专利自动分类研究^*[J]. 数据分析与知识发现, 2017, 1(8): 76-84.
[13]	李姝影,方曙. 测度技术融合与趋势的数据分析方法研究进展^*[J]. 数据分析与知识发现, 2017, 1(7): 2-12.
[14]	翟东升,郭程,张杰,夏军. 基于专利的企业潜在研发伙伴推荐方法研究[J]. 数据分析与知识发现, 2017, 1(3): 10-20.
[15]	翟东升,胡等金,张杰,何喜军,刘鹤. 专利发明等级分类建模技术研究^*[J]. 数据分析与知识发现, 2017, 1(12): 63-73.

Viewed

Full text

Abstract

Cited

Shared

Discussed