图书情报知识 ›› 2022, Vol. 39 ›› Issue (1): 73-83.doi: 10.13366/j.dik.2022.01.073

• 情报、信息与共享 • 上一篇    下一篇

数据驱动的产业技术情报分析方法体系框架构建

霍朝光,卢小宾,杨冠灿,霍帆帆   

  • 出版日期:2022-01-10 发布日期:2022-03-19

The Method Framework for Data-driven Information Analysis Towards Industrial Technology

  • Online:2022-01-10 Published:2022-03-19

摘要: [目的/意义]数据驱动的产业技术情报分析,是数据战略浪潮下的科技尖兵。本文旨在完善现有产业技术情报分析方法体系,进一步融合新兴算法以促进其发展。[研究设计/方法]梳理了目前产业技术情报分析中采用的方法,简述了各种分析方法的研究特点,提出在大数据环境下革新传统情报分析思路,塑造数据驱动的产业技术情报分析模式,构建了数据驱动的产业技术情报分析核心方法体系框架。[结论/发现]面向六大产业技术情报分析目标,针对识别、预测、评估、预警四大情报分析任务,本文构建文本数据、网络数据、图像数据三类核心数据驱动的,囊括文本挖掘、图挖掘、图像挖掘三大方法体系的产业技术情报分析方法框架。[创新/价值]构建了文本数据、网络数据、图像数据三维驱动的产业技术情报分析方法体系框架,凝练文本挖掘、图挖掘、图像挖掘三种非结构数据情报分析模式。

关键词: 情报分析, 产业技术, 文本挖掘, 图挖掘, 图像挖掘, 数据驱动

Abstract: [Purpose/Significance] The Data-driven information analysis of industrial technology is the frontier of science and technology in the wave of data strategy. This paper aims to improve the existing method system of industrial technology information analysis and enhance its development through fusion with new algorithms. [Design/Methodology] This study concluded current methods of industrial technology information analysis and illustrated their research characteristics.It also suggested a transformation of traditional information analysis method and proposed to build a schema for data-driven information analysis of industrial technology. Then it developed a core method framework for industrial technology information analysis. [Findings/Conclusion] Orienting to the six aspects of industrial technology information analysis, aiming at the four tasks of identification, prediction, evaluation and early-warning, this paper constructs an information analysis method framework consisting of text mining, graph mining and image mining. [Originality/Value] This paper constructs a three-dimensional information analysis method framework of industrial technology, which is driven by text data, network data and image data. It also concludes the information analysis modes of three kinds of unstructured data, which are text mining, graph mining and image mining.

Key words: Information analysis, Industrial technology, Text mining, Graph mining, Image mining, Data-driven