A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

Lilan Chen

doi:10.1117/12.3024790

3 April 2024 A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

Lilan Chen

Author Affiliations +

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780Z (2024) https://doi.org/10.1117/12.3024790
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

The Guangdong Provincial Archives stores a large volume of valuable historical original Customs archives which are enormous in number and volume, formal in format, various in types and genres, rich in content, providing precious value for such studies as those of Imperial Chinese and foreign exchanges. It has always been the focus of research in the academic world at home and abroad. This paper, characterized by the inputting and outputting relationship among the entities (proper names of people, places, titles, weights and measures) that are most closely related to the translation of Imperial Guangdong Maritime Customs archives, constructs the knowledge graph of Imperial Guangdong Maritime Customs archival translation. In this paper, representative subgraphs being calculated based on the given node sets, a novel archival translation knowledge graph method is proposed, and the spatial components are integrated into the reinforcement learning framework to help understand the knowledge graph of archival translation. Through establishing the Imperial Guangdong Maritime Customs archival translation theory model, the internal structure and external information of archives are analyzed to obtain a more comprehensive and holistic view of the archival translation tasks. At last, the model proposed in this paper is proved to be reasonable by evaluating the data sets of Imperial Guangdong Maritime Customs archives.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lilan Chen "A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780Z (3 April 2024); https://doi.org/10.1117/12.3024790

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