Link Prediction in Industrial Knowledge Graphs: A Case Study on Football Manufacturing

Abstract

The integration of heterogeneous and unstructured data in Industry 4.0, poses a significant challenge, particularly with advanced manufacturing techniques. To address this issue, Knowledge Graphs (KGs) have emerged as a pivotal technology, yet their deployment often encounters the problem of incompletion due to data diversity and diverse storage formats. This study tackles the challenge of KG completion by applying and evaluating state-of-the-art KG embedding models-ComplEx, DistMult, TransE, ConvKB, and ConvE-within a football manufacturing production line context. Our analysis employs two principal metrics of Mean Reciprocal Rank (MRR) and Hits@N (Hits@10, Hits@3, and Hits@1) to comprehensively assess model performance. Our findings reveal that TransE significantly outperforms its counterparts, achieving an average accuracy of 91%, closely followed by ComplEx and DistMult with accuracies of 87% and 84%, respectively. Conversely, ConvKB and ConvE exhibit lower performance levels, with accuracy values of 79% and 76%. Through rigorous statistical testing, including t-tests, meaningful differences in MRR values across the models have been observed, with TransE leading in MRR and ConvE at the lower end of the spectrum. Our research not only sheds light on the efficacy of various KG embedding models in managing tree-like structured datasets within the manufacturing domain but also offers insights into optimising KGs for improved integration and analysis of data in production lines. These contributions are valuable both from academic research in KG completion and industrial practices aiming to enhance production efficiency and data coherence in advanced manufacturing settings.

Keywords

• Industry 4.0
• knowledge graph completion
• link prediction
• smart manufacturing