Research on Knowledge Graph Embedding Methods for Cement Clinker Pre-Decomposition Process

The pre-decomposition process is a crucial step in cement clinker production, which is influenced by numerous parameters. To gain a deeper understanding of the conditions that lead to different process states, it is necessary to explore the relationships among these factors. Knowledge graphs have emerged as a powerful technique for representing relationships between entities and have been applied to various real-world tasks such as disease diagnosis, recommendation systems, and traffic prediction. In this study, we propose a novel knowledge graph embedding model based on convolutional neural networks, designed specifically for the constructed pre-decomposition knowledge graph. This model aims to uncover hidden relationships within the knowledge graph and provide insights into the process.