图书情报工作

图书情报工作 >

2019 , Vol. 63 >Issue 6: 75 - 84

DOI: https://doi.org/10.13266/j.issn.0252-3116.2019.06.010

情报研究

基于SAO结构的创新解决方案遴选研究——以空气净化技术为例

  • 付芸 ,
  • 汪雪锋 ,
  • 李佳 ,
  • 侯雨佳
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  • 1. 中国科学院文献情报中心 北京 100190;
    2. 北京理工大学管理与经济学院 北京 100081
付芸(ORCID:0000-0001-9975-2007),助理馆员,硕士;李佳(ORCID:0000-0001-6956-876X),硕士研究生;侯雨佳(ORCID:0000-0001-6115-3607),硕士研究生。

收稿日期: 2017-11-13

  修回日期: 2018-08-28

  网络出版日期: 2019-03-20

基金资助

本文系国家自然科学基金面上项目"基于SAO语义挖掘的技术研发合作伙伴识别与选择方法研究"（项目编号：71774012）研究成果之一。

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Research on Selection of Innovative Solutions Based on SAO Structure: A Case Study on Air Purification Technology

  • Fu Yun ,
  • Wang Xuefeng ,
  • Li Jia ,
  • Hou Yujia
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  • 1. National Science Library, Chinese Academy of Sciences, Beijing 100190;
    2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081

Received date: 2017-11-13

  Revised date: 2018-08-28

  Online published: 2019-03-20

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摘要

[目的/意义]面对竞争日益激烈的社会环境，创新是企业的立足之本、生存之道。创新的范围不仅包括在本领域创造新的产品、技术等，还包括将其他领域出现的新产品、新技术引入本领域，且后者更为容易。但随着学科领域专业化程度越来越高，科研人员没有过多精力去掌握本领域之外的知识，因此需要借助科学的方法和技术来探索不同领域知识间深层次的联系。[方法/过程]借鉴LRDI方法的分析流程，提出基于SAO结构的创新解决方案遴选方法，以目标研究领域具体研究问题为出发点，在全领域寻找潜在解决方案，并从技术可行性以及预期效果两方面对这些潜在解决方案进行评价，形成适用于目标研究领域的优先推荐创新解决方案。[结果/结论]以空气净化技术为例开展实证研究，研究结果表明部分创新解决方案已经在空气净化技术领域得到有效应用，进一步证实了本研究方法的可行性和有效性。

关键词： 创新解决方案; SAO结构; 关联文献知识发现(LRDI); 技术路线图; 空气净化技术

本文引用格式

付芸 , 汪雪锋 , 李佳 , 侯雨佳 . 基于SAO结构的创新解决方案遴选研究——以空气净化技术为例[J]. 图书情报工作, 2019 , 63(6) : 75 -84 . DOI: 10.13266/j.issn.0252-3116.2019.06.010

Abstract

[Purpose/significance] Facing the increasingly competitive social environment, innovation is the foundation and the way of existence for enterprises. The scope of the innovation not only includes creating new products, technologies, etc. in the target research field, but also includes introducing new technologies, products, etc. from other research filed into the target field. The latter one is much easier to be accomplished. However, with the increasingly high degree of specialization in every disciplinary field, researchers have little time to grasp the knowledge besides their own research field. So it is needed to use scientific method and technology to explore the deep relationships between knowledge from different research fields. [Method/process] Using the analytical process of LRDI methodology, the paper proposes the research on selection of innovative solutions based on SAO structure, seeking for the potential solutions in whole research field based on the specific problems from target research field. The paper evaluates these potential solutions from the aspects of the technical feasibility and expected results and gives priority to recommend the solutions as innovative solutions for target research field. An exploratory study is conducted on air purification technology for this systematic process. [Result/conclusion] The research shows that some of the selected innovative solutions have been effectively used in air purification field, which also verify the proposed research method is feasible and valid.

Key words： innovative solutions; SAO structure; LRDI; technology roadmap; air purification technology

参考文献

[1] YOON B, PARK I, COH B Y. Exploring technological opportunities by linking technology and products:application of morphology analysis and text mining[J]. Technological forecasting & social change, 2014, 86:287-303.
[2] DAIM T U, RUEDA G, MARTIN H, et al. Forecasting emerging technologies:use of bibliometrics and patent analysis[J]. Technological forecasting & social change, 2006, 73(8):981-1012.
[3] CHESBROUGH H. Open services innovation:rethinking your business to grow and compete in a new era[M]. San Francisco:Jossey-Bass Wiley, 2010.
[4] ZHU D, PORTER A L. Automated extraction and visualization of information for technological intelligence and forecasting[J]. Technological forecasting & social change, 2002, 69(5):495-506.
[5] BARKER D, SMITH D J H. Technology foresight using roadmaps[J]. Long range planning, 1995, 28(2):21-28.
[6] WANG X F, MA P P, HUANG Y, et al. Combining SAO semantic analysis and morphology analysis to identify technology opportunities[J]. Scientometrics, 2017, 111(1):3-24.
[7] YOON J, PARK H, SEO W, et al. Technology opportunity discovery (TOD) from existing technologies and products:a function-based TOD framework[J]. Technological forecasting & social change, 2015, 100:153-167.
[8] KOSTOFF R N. Literature-related discovery and innovation-update[J]. Technological forecasting & social change, 2012, 79:789-800.
[9] SWANSON D R. Fish oil, Raynauds sumdrome, and undiscovered public knowledge[J]. Perspectives in biology and medicine, 1986, 30(1):7-18.
[10] 田瑞强,姚长青,潘云涛. 关联文献的知识发现与创新研究进展[J].情报理论与实践,2013,36(8):117-123.
[11] SWANSON D R. Migraine and magnesium-Ⅱ neglected connections[J]. Perspectives in biology and medicine, 1988, 31(4):526-557.
[12] SWANSON D R. Somatomedin-C and arginine-implicit connections between mutually isolated literatures[J[. Perspectives in biology and medicine, 1990, 33(2):157-186.
[13] SWANSON D R. SMALHEISER N R. Information discovery from complementary literatures:categorizing viruses as potential weapons[J]. Journal of the American Society for Information Science & Technology, 2001,52(10):797-812.
[14] GORDON M D, LINDSAY R K. Toward discovery support systems:a replication,re-examination,and extension of Swanson's work on literature-based discovery of a connection between Raynaud's and fish oil[J]. Journal of the American Society for Information Science & Technology, 1996, 47(2):116-128.
[15] WEEBER M, KLEIN H, BERG L, et al. Using concepts in literature based discovery:simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries[J]. Journal of the American Society for Information Science & Technology, 2001, 52(7):548-557.
[16] STEGMANN J,GROHMANN G. Hypothesis generation guided by co-word clustering[J]. Scientometrics, 2003, 56(1):111-135.
[17] KOSTOFF R N. Literature-related discovery (LRD):Methodology[J]. Technological forecasting and social change, 2008, 75:186-202.
[18] KOSTOFF R N, PATEL U. Literature-related discovery and innovation:chronic kidney disease[J]. Technological forecasting & social change, 2015, 91:341-351.
[19] KOSTOFF R N, BLOCK J A, STUMP J A, et al. Literature-related discovery (LRD):potential treatments for Raynaud's phenomenon[J]. Technological forecasting & social change, 2008, 75(2):203-214.
[20] KOSTOFF R N. Literature-related discovery (LRD):potential treatments for cataracts[J]. Technological forecasting and social change, 2008, 75(2):215-225.
[21] KOSTOFF R N, BRIGGS M B. Literature-related discovery (LRD):potential treatments for Parkinson's disease[J]. Technological forecasting & social change, 2008, 75(2):226-238.
[22] KOSTOFF R N, BRIGGS M B, LYONS T J. Literature-related discovery (LRD):potential treatments for multiple sclerosis[J]. Technological forecasting & social change, 2008, 75(2):239-255.
[23] KOSTOFF R N. Literature-related discovery:potential treatments and preventatives for SARS[J]. Technological forecasting & social change, 2011, 78:1164-1173.
[24] KOSTOFF R N, SOLKA J L, RUSHENBERG R L, et al. Literature-related discovery (LRD):water purification[J]. Technological forecasting & social change, 2008, 75(2):256-275.
[25] VITAVIN I, KATSUHIDE F, YUYA K, et al. Finding linkage between technology and social issues:a literature based discovery approach[C]//Proceedings of PICMET' 12:Technology Management for Emerging Technologies. Vancouver:IEEE, 2012.
[26] 曹志杰,冷伏海. 非相关文献知识发现方法在航天科技情报研究中的应用分析[J]. 情报理论与实践,2008(4):569-572.
[27] 黄水清,马俊岭. 汉语社会科学文献非相关文献知识发现的实证研究——以农业经济学文献为例[J]. 中国图书馆学报,2009(4):31-38.
[28] MOEHRLE M G, WALTER L, GERITZ A, et al. Patent-based inventor profiles as a basis for human resource decisions in research and development[J]. R&D management, 2015,35(5):513-524.
[29] GERKEN J M, MOEHRLE M G. A new instrument for technology monitoring:novelty in patents measured by semantic patent analysis[J]. Scientometrics, 2012, 91(3):645-670.
[30] YOON J, PARK H, KIM K. Identifying technological competition trends for R&D planning using dynamic patent maps:SAO-based content analysis[J]. Scientometrics, 2013, 94(1):313-331.
[31] 郭俊芳, 汪雪锋, 邱鹏君,等. 基于SAO分析的技术路线图构建研究[J]. 科学学研究, 2014, 32(7):976-1002.
[32] WANG X, QIU P, ZHU D, et al. Identification of technology development trends based on subject-action-object analysis:the case of dye-sensitized solar cells[J]. Technological forecasting & social change, 2015, 98:24-46.
[33] CHOI S, YOON J, KIM K, et al. SAO network analysis of patent for technology trends identification:a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells[J]. Scientometrics, 2011, 88(3):863-883.
[34] YOON J, KIM K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks[J]. Scientometrics, 2011, 88(1):213-228.
[35] 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.
[36] 温亮,邱鹏君,马萍萍,等.基于SAO语义分析的潜在技术合作伙伴识别[J]. 北京理工大学学报(社会科学版), 2017(4):91-96.
[37] BERGMANN I, BUTZKE D, WALTER L, et al. Evaluating the risk of patent infringement by means of semantic patent analysis:the case of DNA chips[J]. R&D management, 2008, 38(5):550-562.
[38] PARK H, YOON J, KIM K. Identifying patent infringement using SAO based semantic technological similarities[J]. Scientometrics, 2012, 90(2):515-529.
[39] CHOI S, KANG D, LIM J, et al. A fact-oriented ontological approach to SAO-based function modeling of patents for implementing function-based technology database[J]. Expert systems with applications 2012, 39(10):9129-9140.
[40] 吴菲菲, 李倩, 黄鲁成. 基于专利SAO结构的技术应用领域识别方法研究[J]. 科研管理2014, 35(6):1-7.
[41] 汪雪锋, 付芸, 邱鹏君, 等. 基于SAO分析的R&D合作伙伴识别研究[J]. 科研管理, 2015, 36(10):19-27.
[42] 张嶷, 汪雪锋, 朱东华, 等. "主题词簇"方法研究——英文科技文献主题词清洗、合并与聚类[J]. 科学学研究, 2013(11):1615-1622.
[43] VICENTE-GOMILA J M. The contribution of syntactic-semantic approach to the search for complementary literatures for scientific or technical discovery[J]. Scientometrics, 2014, 100(3):659-673.
[44] 刘恢. 可见光响应光催化剂及其分解水的研究[D]. 上海:上海交通大学, 2008.
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