A Novel Case Knowledge Representation Model for Maritime Collision Accidents

DOI

10.2991/978-94-6463-502-7_14How to use a DOI?

Keywords

Knowledge representation; Ontology; Convolution neural network

Abstract

It is of great significance to research a method to standardize the management of maritime collision case knowledge with high complexity, poor standardization and weak regularity. In this paper, a novel case knowledge representation model is proposed based on ontology and convolution neural network. Firstly, an automatic case knowledge extraction model is proposed, which can be used to improve the efficiency of knowledge extraction. Secondly, under the condition of extracting case knowledge, a case knowledge representation model is proposed based on improved ontology, which can be used to improve the scientific and standardized case knowledge management. Finally, the effectiveness of the proposed is illustrated by a case study based on maritime collision accident in the South China Sea.

Copyright

© 2024 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.

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TY  - CONF
AU  - Zhiying Chen
AU  - Ke Shi
PY  - 2024
DA  - 2024/08/31
TI  - A Novel Case Knowledge Representation Model for Maritime Collision Accidents
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 126
EP  - 132
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_14
DO  - 10.2991/978-94-6463-502-7_14
ID  - Chen2024
ER  -