• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Technological forecasting and social change >Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction
【24h】

Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction

机译：通过基于生成的地形图和链接预测可视化专利信息来探索技术机会

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The shortening lifetime of technology requires companies to make intensive efforts to continuously explore new technology. Although many researchers have proposed visualization methods to find technology opportunities, little attention has been paid to present detailed directions of technology development with specified characteristics of technology. Thus, this research aims to suggest a systematic approach to conducting technology opportunity analysis by visualizing patent information, such as patent documents and citation relationships. First, keywords that explain core concepts, functions, and so on are extracted from collected patent documents by text mining. Second, patents are visualized in a two-dimensional space, and vacant cells are identified with their estimated keyword vectors by generative topographic mapping (GTM). Third, since many vacant cells will be potential candidates for developing new technologies, link prediction tools can choose promising vacant cells to connect existing cells with potential, but not yet existent, cells. Finally, the results of prediction are tested by comparing the predicted cells with the actual developed cells. The research reported in this paper is based in three technologies that have emerging, stable, and declining patterns, in order to illustrate the proposed approach, and investigate in which types it is relevant. It is found that the proposed approach provided a good prediction performance in the case of a technology that has a stable pattern. In addition, among link prediction methods, a semantic similarity-based approach showed better prediction results than a machine learning technique due to modest data availability for training. Thus, the results of this research can help R&D managers plan and evaluate R&D projects for technology development.
机译：技术寿命的缩短要求公司加大力度以不断探索新技术。尽管许多研究人员提出了可视化方法来寻找技术机会，但是很少有人关注具有特定技术特征的详细技术发展方向。因此，本研究旨在提出一种通过可视化专利信息（例如专利文件和引用关系）进行技术机会分析的系统方法。首先，通过文本挖掘从收集的专利文件中提取解释核心概念，功能等的关键字。其次，在二维空间中可视化专利，并通过生成的地形图（GTM）使用其估计的关键字向量来识别空单元。第三，由于许多空单元将是开发新技术的潜在候选者，因此链接预测工具可以选择有前途的空单元，以将现有单元与潜在但尚未存在的单元连接起来。最后，通过将预测的细胞与实际发育的细胞进行比较来测试预测的结果。本文所报告的研究基于三种具有新兴，稳定和下降模式的技术，以便说明所提出的方法，并研究与哪种类型相关。发现在具有稳定模式的技术的情况下，所提出的方法提供了良好的预测性能。此外，在链接预测方法中，基于语义相似性的方法由于训练所需的数据可用性适中，因此比机器学习技术显示出更好的预测结果。因此，这项研究的结果可以帮助研发经理规划和评估用于技术开发的研发项目。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号