scholarly journals Construction of knowledge graph of maritime dangerous goods based on IMDG code

2020 ◽  
Vol 2020 (13) ◽  
pp. 361-365
Author(s):  
Qi Zhang ◽  
Yuan Q Wen ◽  
Dong Han ◽  
Fan Zhang ◽  
Chang S Xiao
2019 ◽  
Vol 11 (10) ◽  
pp. 2849 ◽  
Author(s):  
Qi Zhang ◽  
Yuanqiao Wen ◽  
Chunhui Zhou ◽  
Hai Long ◽  
Dong Han ◽  
...  

Dangerous goods occupy an important proportion in international shipping, and government and enterprises pay a lot of attention to transport safety. There are a wide variety of dangerous goods, and the knowledge involved is extensive and complex. Organizing and managing this knowledge plays an important role in the safe transportation of dangerous goods. The knowledge graph is a mass of brand-new knowledge management technologies that provide powerful technical support for integrating domain knowledge and solving the problem of the “knowledge island.” This paper first introduces the knowledge of maritime dangerous goods (MDG); constructs a three-layer knowledge structure of MDG, dividing this knowledge into two categories; uses ontology to express the concepts, entities, and relations of MDG; and puts forward the representation methods of the conceptual layer and entity layer and designs them in detail. Finally, the knowledge graph of maritime dangerous goods (KGMDG) is constructed. Furthermore, we demonstrate the knowledge visualization, retrieval, and automatic judgment of segregation requirement based on KGMDG. It is proved that KGMDG does not only help to simplify the retrieval process of professional knowledge and to promote intelligent transportation but is also conducive to the sharing, dissemination, and utilization of MDG knowledge.


2021 ◽  
Author(s):  
An Fang ◽  
Pei Lou ◽  
Jiahui Hu ◽  
Wanqing Zhao ◽  
Ming Feng ◽  
...  

BACKGROUND Pituitary adenoma is one of the most common central nervous system tumors. The diagnosis and treatment of pituitary adenoma are still very difficult. Misdiagnosis and recurrence occur from time to time, and experienced neurosurgeons are in serious shortage. Knowledge graphs can help interns quickly understand the medical knowledge related to pituitary tumor. OBJECTIVE The aim of this paper is to integrate the data of pituitary adenomas from reliable sources and construct a knowledge graph, and use the knowledge graph for knowledge discovery. METHODS A method of constructing a knowledge graph of diseases was introduced and used to build a knowledge graph for pituitary adenoma (KGPA). The schema of the KGPA was manually constructed. Information of pituitary adenoma were automatically extracted from EMR and the medical websites through the CRF model and web wrappers we designed. An entity fusion method was proposed, based on the head and tail entity fusion models, to fuse the data from heterogeneous sources. The disease entities were standardized to ICD-10. RESULTS Data was extracted from 300 EMRs of pituitary adenoma and 4 medical portals. Entity fusion was carried out by using the data fusion model we proposed. The accuracy of the head and tail entity fusion were more than 97%. Part of the triples were selected for evaluation, and the accuracy was 95.4%. CONCLUSIONS This paper introduced an approach to construct KGPA and proposed a data fusion method suitable for medical data. The evaluation results show that the data in KGPA is of high quality. The constructed KGPA can help physicians in their clinical practice.


Author(s):  
Chuqiao Yang ◽  
Haiyan Wang ◽  
Jingyan Sai ◽  
Peiwei Zhang ◽  
Mengyao Yan

2021 ◽  
Author(s):  
Jie Zhao ◽  
Aiyu Wang ◽  
Fangfang Su ◽  
Yanyan Chen ◽  
Honghai Feng

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