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MSc Thesis Defense Announcement of Joseph El-Ghaname:"Automotive Fault Detection using Knowledge Graph Embedding "

Tuesday, May 24, 2022 - 10:30 to 12:30


The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Joseph El-Ghaname 

Date: Tuesday, May 24, 2022 
Time:  10:30 am – 12:30 pm 
Passcode: If interested in attending this event, contact the Graduate Secretary at with sufficient notice before the event to obtain the passcode


Automotive industry has witnessed expensive, dangerous, and potentially fatal recalls over the years. With the rollout of new electric vehicles (EV), additional complexity is brought into the manufacturing process. Companies must use expensive fault detection systems to identify defects in the assembly process. Knowledge-based approaches for fault detection in manufacturing and assembly systems reflect modern practices in Industry 4.0. In this thesis, we examine the feasibility of using knowledge graphs (KG) for their effectiveness in detecting faults in a custom dataset. We implement a KG Completion (KGC) algorithm and compare different KG Embedding (KGE) models. Furthermore, we measure and compare the Mean Reciprocal Rank (MRR) and Hits@k to evaluate the algorithm based on various KGE approaches and models. Our findings from our experiments and thesis paves a new pathway for vehicle manufacturers and car makers, allowing for a feasible and comprehensive fault detection system and framework. By combining state-of-the-art KGE models and a first-hand case study involving an electric vehicle knowledge graph dataset (EV-KG), this thesis solidifies future KG-related fault detection research in the field and opens numerous opportunities for further development and application in the real-world industry.  
Keywords: Knowledge Graphs, Knowledge Graph Completion, Knowledge Graph Embedding, fault detection, link prediction.

MSc Thesis Committee:  

Internal Reader: Dr. Pooya Moradian Zadeh         
External Reader: Dr. Narayan Kar 
Advisor: Dr. Ziad Kobti 
Chair: Dr. Dan Wu 

 MSc Thesis Defense Announcement   

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