摘要
传统水上交通事故研究主要利用事故案例挖掘事故致因和事故间相互影响关系,在反映事故全过程和人-船-货-环-管-信等要素间相互作用方面存在不足。为此,以船舶碰撞事件为例,基于多源异构信息构建了水上交通事故领域船舶碰撞事故防控知识图谱。充分考虑“事件-时空行为-事件致因-事件后果-责任主体-处置决策”事故组成要素,提出了船舶碰撞事故知识标准化框架;构建了基于中文全词掩码预训练语言模型(Chinese-bert-wwm)的知识抽取模型;依托Neo4j数据库,构建了船舶碰撞事故防控知识图谱,图谱包括15种实体类型和39种关系类型,包含35784个实体和325097个关系。所提船舶碰撞事故防控知识图谱,在规模上显著优于现有水上交通领域的知识图谱,知识自动抽取的精度达到85%,明显高于隐马尔可夫模型(hidden Markov model,HMM)和条件随机场(conditional random field,CRF)等模型。其中,“船舶”“人员特征”“时间”“人员”和“法律法规”类实体上下文推理的F1值分别为95%、91%、89%、88%和88%,关系识别的F1值达到94%。以上结果表明:通过Chinese-bert-wwm模型提取船舶碰撞事故的语义特征,增强了知识抽取模型的泛化能力。本研究不仅可以支持对船舶碰撞事故知识表示、海事执法人员对事故的回溯及利用,也有助于提高水上交通系统的管理效能。
Traditional research on water transportation accidents mainly focuses on exploring the causative factors and corresponding complex relationship with various accidents,which is insufficient in reflecting the evolution of traffic accidents and the complicated interactions between elements including people,vessels,cargo,environment,administration,and information in the maritime system.To fill the gap,this paper proposes a methodology for developing a water transportation knowledge graph based on multi-source heterogeneous information and applies it to the accident prevention and control strategies development.A framework for ship collision knowledge is designed,considering the components of accidents,e.g.,event,spatiotemporal ship behavior,maritime accidents causative factors,accidents consequences,corresponding responsibility roles,and disposal decision-making.A knowledge extraction model is employed to extract the maritime safety knowledge,which is based on Chinese Bidirectional Encoder Representations from Transformers Whole Word Masking and is named as Chinese-bert-wwm model.Thirdly,the SCPCKG(ship collision prevention and control knowledge graph)is developed based on the Neo4j database,which contains 35784 entities from 15 entity types and 325097 relationships from 39 relationship types.The scale of the SCPCKG is significantly larger than that of existing knowledge graphs in the field of water transportation,and the accuracy of automated knowledge extraction based on the proposed SCPCKG reaches 85%,which is higher than the existing models,such as Hidden Markov Models(HMMs)and Conditional Random Fields(CRFs).Specifically,the F1-score value for identifying“ship”,“person characteristics”,“time”,“person”,and“laws”entities reaches 95%,91%,98%,88%,and 88%,respectively;the F1-score value of relationship extraction reaches 94%.The results show that the proposed Chinese-bert-wwm model can enhance the generalized capability of the knowledge extraction model by extracting the semantic features of ship collision accidents from the accident reports,and the proposed SCPCKG can be used for the knowledge representation of ship collision accidents and inversion of accidents for maritime administrators,improving the effectiveness of the water transportation management.
作者
余红楚
郭正
魏天明
许磊
方庆龙
YU Hongchu;GUO Zheng;WEI Tianming;XU Lei;FANG Qinglong(School of Navigation,Wuhan University of Technology,Wuhan 430063,China;Sanya Science and Education Innovation Park,Wuhan University of Technology,Sanya 572025,Hainan,China;National Engineering Research Center of Geographic Information System,China University of Geosciences,Wuhan 430078,China)
出处
《交通信息与安全》
北大核心
2025年第3期10-23,共14页
Journal of Transport Information and Safety
基金
国家重点研发计划项目(2022YFC3302703)
国家自然科学基金项目(42101429、42371415)
中国科学技术协会青年人才托举工程项目(YESS20220491)
海南省教育厅项目(Hnjg2024-284)资助。
