TY - GEN
T1 - A Method for Constructing Engineering-Biology Domain Knowledge Network Oriented Toward Collective Intelligence Innovation Design
AU - Liu, Haibo
AU - Gong, Lin
AU - Zhang, Xianpeng
AU - Chang, Xuqiang
AU - Li, Hanhun
AU - Jiang, Weipeng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In the context of the era of information, the growth of personalized user needs and the development of Internet plus technology have brought new challenges and opportunities to product innovation design. As an emerging design mode, collective intelligence design can acquire design knowledge through group wisdom and extensive range to improve the market adaptability of products and the innovation ability of enterprises. As a kind of long-distance knowledge, biology domain knowledge can effectively play the incentive role of cross-domain knowledge in the collective intelligence design process. However, there is currently no effective way of combining engineering with biology domain knowledge to underpin the design process. To address this issue, we propose a method for constructing an engineering-biology domain knowledge network for collective intelligence innovation design: A cross-domain knowledge representation model is constructed to achieve unified representation of engineering and biology domain knowledge; By using cross-domain entity extraction and relationship generation methods, an engineering-biology domain knowledge network is automatically constructed. This method provides a new perspective for cross-domain knowledge motivation and inspiration in collective intelligence innovation design. Through the representation and relationship generation of cross-domain knowledge, our engineering-biology domain knowledge network can provide designers with long-distance design knowledge incentives, improving the quality and innovation of generated solutions.
AB - In the context of the era of information, the growth of personalized user needs and the development of Internet plus technology have brought new challenges and opportunities to product innovation design. As an emerging design mode, collective intelligence design can acquire design knowledge through group wisdom and extensive range to improve the market adaptability of products and the innovation ability of enterprises. As a kind of long-distance knowledge, biology domain knowledge can effectively play the incentive role of cross-domain knowledge in the collective intelligence design process. However, there is currently no effective way of combining engineering with biology domain knowledge to underpin the design process. To address this issue, we propose a method for constructing an engineering-biology domain knowledge network for collective intelligence innovation design: A cross-domain knowledge representation model is constructed to achieve unified representation of engineering and biology domain knowledge; By using cross-domain entity extraction and relationship generation methods, an engineering-biology domain knowledge network is automatically constructed. This method provides a new perspective for cross-domain knowledge motivation and inspiration in collective intelligence innovation design. Through the representation and relationship generation of cross-domain knowledge, our engineering-biology domain knowledge network can provide designers with long-distance design knowledge incentives, improving the quality and innovation of generated solutions.
KW - collective intelligence innovation design
KW - cross-domain knowledge network
KW - knowledge inspiration
UR - http://www.scopus.com/pages/publications/105001920029
U2 - 10.1109/ICaMaL62577.2024.10919520
DO - 10.1109/ICaMaL62577.2024.10919520
M3 - Conference contribution
AN - SCOPUS:105001920029
T3 - 2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024
BT - 2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024
Y2 - 7 August 2024 through 9 August 2024
ER -