Empirical Instances Knowledge Mining Method for Ship Design Domain

DOI

10.2991/978-94-6463-172-2_223How to use a DOI?

Keywords

Knowledge mining; ship design domain; empirical instances; natural language processing

Abstract

Knowledge mining based on ship instances information has an important support role for ship design. In order to obtain easy to use knowledge from ship design instances composed of natural language text, this paper proposes an empirical case knowledge mining method for ship design domain. The method uses natural language processing techniques to structure the empirical instances according to the characteristics of the ship design domain and the structural characteristics of the empirical instances; and then combines knowledge clustering and variable precision rough set theory to obtain ship design rules from the instances. The experimental analysis of the data set provided by shipyards shows the feasibility and effectiveness of this method to obtain ship design knowledge rules.

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.

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TY  - CONF
AU  - Tao Xie
PY  - 2023
DA  - 2023/06/30
TI  - Empirical Instances Knowledge Mining Method for Ship Design Domain
BT  - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023)
PB  - Atlantis Press
SP  - 2003
EP  - 2010
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-172-2_223
DO  - 10.2991/978-94-6463-172-2_223
ID  - Xie2023
ER  -