Construction and Application of Knowledge Graph for railway rolling stock failure tree

Ragala, Zaynabe; Retbi, Asmaâ; Bennani, Samir

doi:10.1109/ictmod55867.2022.10041881

Cited by 4 publications

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A literature review on the applications of artificial intelligence to European rail transport safety

Hadj‐Mabrouk

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

In accordance with the current European railway regulations and particularly the two directives relating to the interoperability (Directive (EU) 2016/797) and safety (Directive (EU) 2016/798) of the railway system, this literature review proposes to classify artificial intelligence (AI) applications by distinguishing the structural elements (Infrastructure, Energy, Control‐Command‐Signalling and Rolling Stock) and the functional elements (Operation and Traffic Management, Maintenance and Telematics Applications) of the European railway system. Several “classic” AI techniques are implemented, including machine learning (supervised, semi‐supervised, unsupervised), deep learning such as artificial neural networks (ANN), natural language processing (NLP), case‐based reasoning (CBR), etc. However, the inadequacy of these approaches to capitalize, share and reuse the knowledge involved has oriented research towards the development of new approaches based on ontologies and knowledge graphs. This study shows that the stages of data acquisition, modeling, processing and interpretation pose a crucial problem in rail transport. In addition, with complex models described as “black boxes”, it is difficult to understand how the internal reasoning mechanisms of the AI system impact the solution and predictions. The new explainable AI (XAI) approach can possibly provide an element of response to this problem.

show abstract

A literature review on the applications of artificial intelligence to European rail transport safety

Hadj‐Mabrouk

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

A Multivariate Heterogeneous O&M Knowledge Base Construction Method Based on Cloud Platform

Li,

Chen,

Wang

et al. 2024

2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP)

View full text Add to dashboard Cite

Improving Recommendation System by using a knowledge Graph Database for Maintenance of Rolling Stock

Ragala,

Retbi,

Bennani

2023

2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD)

View full text Add to dashboard Cite

Construction and Application of Knowledge Graph for railway rolling stock failure tree

Cited by 4 publications

References 11 publications

A literature review on the applications of artificial intelligence to European rail transport safety

A literature review on the applications of artificial intelligence to European rail transport safety

A Multivariate Heterogeneous O&M Knowledge Base Construction Method Based on Cloud Platform

Improving Recommendation System by using a knowledge Graph Database for Maintenance of Rolling Stock

Contact Info

Product

Resources

About