Construction of Knowledge Graph for Flag State Control (FSC) Inspection for Ships: A Case Study from China

Abstract

The flag state control (FSC) inspection is an important measure to ensure maritime safety. However, it is difficult to improve ship safety management efficiency using data mining due to the scattered and multi-source ship inspection knowledge. In this paper, the emerging knowledge graph technology is used to integrate multi-source knowledge for the FSC inspection. Firstly, an ontology model is built to systematically describe the knowledge and guide the construction of the data layer of the knowledge graph. Then, the BERT-BiGRU-CRF model is used to extract entities from the unstructured data of the FSC inspection. The extracted results are associated with structured and semi-structured data and stored in the graph database Neo4j to construct the knowledge graph. In addition, a case study of the FSC inspection knowledge graph of Dafeng Port in Yancheng, China, is conducted to verify the strength of the proposed method. The results show that the knowledge graph can correlate trivial knowledge and benefit the efficiency of the FSC inspection. Moreover, the knowledge graph can reflect the deficiency characteristics of ships and support the safety management of water transportation.

Keywords

• maritime safety
• flag state control inspection
• knowledge graph
• knowledge extraction
• BERT-BiGRU-CRF model