专利分类序列和文本语义表示视角下的技术融合预测研究

doi:10.3772/j.issn.1000-0135.2022.06.006

情报学报

2022, Vol. 41

Issue (6): 609-624 DOI: 10.3772/j.issn.1000-0135.2022.06.006

情报技术与应用

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专利分类序列和文本语义表示视角下的技术融合预测研究

张金柱, 李溢峰

南京理工大学经济管理学院信息管理系，南京 210094

Technology Convergence Prediction by the Semantic Representation of Patent Classification Sequence and Text

Zhang Jinzhu, Li Yifeng

Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094

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摘要为了丰富专利分类的网络和文本语义表示，实现两者更有效的语义融合，提高技术融合预测效果，提出基于专利分类序列和文本语义表示的技术融合预测方法。首先，综合考虑专利分类位置及其上下文语境，直接对专利分类序列进行语义表示，提出基于专利分类序列语义表示的技术融合预测方法；其次，根据专利分类在序列中的重要性排序研究专利分类文本分配方法，形成基于专利分类文本语义表示的技术融合预测方法；在此基础上，设计多种特征融合方法，提出融合专利分类序列结构和文本内容语义表示的技术融合预测方法；最后，基于链路预测的理论和方法对提出的多种技术融合预测方法进行定量评价。在无人机领域的实验证实，专利分类序列语义表示模型的效果明显优于其他网络表示学习方法；依据重要性排序的专利分类文本赋予方式优于文本平均分配方式，基于此的专利分类文本语义表示能更好地进行技术融合预测；“SVM（support vector machine）+哈达玛积”的特征融合方法在所有方法中表现最优，较单一方法均有提高。本文提出的方法能够提高技术融合预测的效果，更好地为技术布局、技术研发提供借鉴和参考。

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关键词 ：技术融合, 预测, 表示学习, 专利分类序列, 专利分类文本

收稿日期: 2021-07-02

基金资助:国家自然科学基金面上项目“基于表示学习的专利信息语义融合与深度挖掘研究”（71974095）；江苏省研究生科研与实践创新计划项目“基于技术影响力和融合发展性的技术融合关系价值评估研究”（SJCX21_0159）。

作者简介: 张金柱，男，1983年生，博士，副教授，博士生导师，主要研究领域为科技大数据分析与挖掘、信息组织和信息检索，E-mail：zhangjinzhu@njust.edu.cn；李溢峰，男，1997年生，硕士研究生，主要研究领域为数据分析、技术趋势分；

引用本文:

张金柱, 李溢峰. 专利分类序列和文本语义表示视角下的技术融合预测研究[J]. 情报学报, 2022, 41(6): 609-624.
Zhang Jinzhu, Li Yifeng. Technology Convergence Prediction by the Semantic Representation of Patent Classification Sequence and Text. 情报学报, 2022, 41(6): 609-624.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.06.006 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I6/609

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