Research on Construction Method for Automatic Classification of Group Enterprise R&D Resources Space
- DOI
- 10.2991/978-94-6463-010-7_54How to use a DOI?
- Keywords
- Group Enterprise; R&D Resources; Resource Space
- Abstract
In order to improve the organization and management ability of R&D resources in the group enterprise, and realize the efficient utilization of R&D resources, this paper puts forward the design resource space automatic construction classification method based on the analysis of the space construction requirements of R&D resources in the group enterprise. Machine learning is used to extract keywords from R&D resources, and semantic maps are built based on the semantic similarity of keywords. Hierarchical clustering is implemented through a community detection algorithm. Then, based on the BRET-LEAM model, the mapping of R&D resources to resource space is achieved, and the construction of multidimensional R&D resource space automatic classification is finally completed. Finally, an instance of group enterprise R&D resources automatic classification is built.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Lei Wang AU - Qingpeng Wang AU - Hongyu Shao AU - Li Li AU - Sizhe Pan PY - 2022 DA - 2022/12/02 TI - Research on Construction Method for Automatic Classification of Group Enterprise R&D Resources Space BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 521 EP - 529 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_54 DO - 10.2991/978-94-6463-010-7_54 ID - Wang2022 ER -