SS08: Research on the Construction of Ship Product Standard Knowledge Graph and Application
In order to help the digital and intellectual transformation of the shipbuilding industry, and considering that ships and upstream and downstream industries are characterized by multi-species, small batch, discrete, etc., as well as the role of standards as a guide for industrial development, a set of structured, friendly and easy-to-operate standard system for the shipbuilding industry is urgently needed. Knowledge map is a semantic network used to describe the relationship between internships, through the graphical display of things or concepts in the real world and their interrelationships, the formation of intuitive, clear, reasoning and calculating the network knowledge structure, for the display of standard knowledge, the construction of the standard system provides a new set of methods. This paper proposes an innovative method to construct a knowledge map of ship product standards, adopting the bottom-up strategy and constructing it step by step according to the standardized structure of ship product standards, which not only ensures that the logical relationship between standards is accurately mapped, but also significantly improves the intelligent level of knowledge management and application. Through this method, the standard knowledge can be organized and managed more systematically, and the accessibility and operability of the standards can be improved, thus providing strong support for the digital and intellectual transformation of the shipping industry. And the standard of "Marine External Fire Centrifugal Pump" is used as an example for application verification, which proves the effectiveness of the standard text modeling method based on knowledge graph. This knowledge graph-based modeling method can not only improve the readability and ease of use of the standard, but also lay a solid foundation for the intelligent application of the standard.
Authors
- Yuting Yang (ORCID: https://orcid.org/0000-0003-2077-2780)
- Xin Zhao (ORCID: https://orcid.org/0000-0001-6310-2715)
- Tao Yin (ORCID: https://orcid.org/0000-0001-7797-6886)
- Chao Gao (ORCID: https://orcid.org/0000-0002-2799-9788)
- Jingyao Wang (ORCID: https://orcid.org/0000-0003-1782-8704)
- Jiabin Chen (ORCID: https://orcid.org/0000-0002-6859-4293)
Publication Details
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1109/ickg63256.2024.00065
- Primary Topic
- Advanced Computational Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China