Research Article
Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight
@INPROCEEDINGS{10.4108/eai.26-5-2023.2337248, author={Lian Liu and jun Wang and dan Wu and xingde Huang and DeXiang Jia and Aiqiang Pan}, title={Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight}, proceedings={Proceedings of the 4th International Conference on Education, Knowledge and Information Management, ICEKIM 2023, May 26--28, 2023, Nanjing, China}, publisher={EAI}, proceedings_a={ICEKIM}, year={2023}, month={9}, keywords={technological foresight; knowledge graph; electric vehicles deep neural networks}, doi={10.4108/eai.26-5-2023.2337248} }- Lian Liu
jun Wang
dan Wu
xingde Huang
DeXiang Jia
Aiqiang Pan
Year: 2023
Research on the Method of Integrating Knowledge Graph and Deep Neural Network for Automotive Technology Foresight
ICEKIM
EAI
DOI: 10.4108/eai.26-5-2023.2337248
Abstract
Realizing precise foresight of emerging technologies in the field of electric vehicles helps the company to advance its layout in this field, seize the technological commanding heights, and empower high-level technological self-reliance and self-reliance. This study is based on knowledge graph technology and constructs a technical knowledge graph based on the relationships and attributes between scientific and technological papers in the field. It revolves around the three main characteristics of novelty, social impact, and fundamental innovation in the field of electric vehicle technology, and constructs a complete and quantifiable indicator system for new electric vehicle technologies. The knowledge graph is used to extract feature values of each indicator, relying on deep neural network algorithms, Train the industry's emerging technology foresight model to achieve precise foresight for the development of new electric vehicle technologies. This study can provide valuable reference for technology foresight in the field of electric vehicles and support industrial development decisions.
