A Novel Approach for the Analysis of Ship Pollution Accidents Using Knowledge Graph

Junlin Hu, Weixiang Zhou, Pengjun Zheng () and Guiyun Liu ()
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Junlin Hu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Weixiang Zhou: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Pengjun Zheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Guiyun Liu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China

Sustainability, 2024, vol. 16, issue 13, 1-26

Abstract: Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec’s word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection.

Keywords: ship pollution accidents; knowledge graph; BERT–BiLSTM–CRF; Neo4j; environmental protection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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