图书情报工作 ›› 2022, Vol. 66 ›› Issue (10): 33-44.DOI: 10.13266/j.issn.0252-3116.2022.10.003

所属专题: 创新驱动战略下的技术预测方法与实践

• 专题:创新驱动战略下的技术预测方法与实践 • 上一篇    下一篇

基于深度学习与语义挖掘的技术创新组合识别与追踪

周潇1, 许银彪1, 史益2   

  1. 1. 西安电子科技大学经济与管理学院 西安 710126;
    2. 西安石油大学计算机学院 西安 710065
  • 收稿日期:2021-11-21 修回日期:2022-03-29 出版日期:2022-05-20 发布日期:2022-06-01
  • 作者简介:周潇,副教授,博士,E-mail:belinda1214@126;许银彪,硕士研究生;史益,助教,硕士。
  • 基金资助:
    本文系国家自然科学基金青年基金项目"基于多数据源融合的新兴技术创新路径识别与动态选择研究"(项目编号:71704139)和陕西省自然科学基金青年基金项目"面向企业需求的新兴技术创新组合识别与传递系统的构建研究"(项目编号:2019JQ-661)研究成果之一。

Identifing and Tracing Technological Innovation Combination Based on Deep Learning and Semantic Mining

Zhou Xiao1, Xu Yinbiao1, Shi Yi2   

  1. 1. School of Economics and Management, Xidian University, Xi'an 710126;
    2. College of Computer Science, Xi'an Shiyou University, Xi'an 710065
  • Received:2021-11-21 Revised:2022-03-29 Online:2022-05-20 Published:2022-06-01

摘要: [目的/意义]随着战略型新兴技术产业的迅猛发展,如何识别具有潜在协同效应的技术创新组合、厘清组合中核心的创新关系,是有效规划产业发展路线、提升产业竞争优势的重要前提。[方法/过程]在技术组合进化理论的指导下,结合深度学习、SAO语义挖掘和CFDP算法,提出一种基于专利数据的技术创新组合与演化关系的识别方案。该研究方案共分为3个步骤:首先基于关键词与专利分类号构建领域检索策略,并实现对获取数据的清洗和分词。随后,通过Word2Vec构建领域技术主题的词向量语义网络,并利用CFDP算法识别出潜在创新要素及组合方式。最后,深入挖掘各组合中核心的SAO结构,通过LSTM深度学习算法对其演化关系进行分类,挖掘技术的核心创新方式,进而有效甄别领域潜在的技术机会。[结果/结论]以语音识别领域为例,通过对该领域DII专利文本数据的深入挖掘,识别并追踪5个潜在的技术创新组合及核心创新方式。研究发现,当前我国语音识别领域在智能芯片设计、语音识别算法、新场景和应用等方面有较大的创新潜力。

关键词: 技术创新组合识别, 深度学习, SAO法, 语义挖掘, 专利分析

Abstract: [Purpose/Significance] With the rapid development of strategic emerging technology industries, how to identify technological innovation combinations with potential synergistic effect and clarify the core innovation relationships in the combination is an important prerequisite for effectively planning industrial development routes and enhancing industrial competitive advantages.[Method/Process] Guided by the theory of technology portfolio evolution, this paper based on patent data and proposed a recognition scheme of technological innovation combinations and evolution relationships, which combined algorithms such as deep learning, SAO semantic mining and CFDP. The study protocol was divided into 3 steps:The first step was to design a domain search strategy based on keywords and patent classification numbers and completed the cleaning and word segmentation of the acquired data; Then the study got the word vector semantic network of the technical topics in the domain through Word2Vec, and used the CFDP algorithm to identify potential innovation elements and combination methods; Finally, it deeply explored the core SAO structures in each portfolio, classified their evolutionary relationships through the LSTM deep learning algorithm, and explored the core innovation approach of technology, so as to effectively discover the potential technology chance in the domain.[Result/Conclusion] Taking the field of speech recognition as an example, through in-depth mining of DII patent text data in this field, the study has identified and tracked five types of potential technological innovation combinations and core innovation methods. And the study finds that the current speech recognition field, which is in the smart chip design, speech recognition algorithms, new scenarios and applications, has great potential for technological innovation in China.

Key words: technological innovation combination identification, deep learning, SAO, semantic mining, patent analysis

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