Multimodal Knowledge Graph-Enabled Steel Product Development

The development of industrial internet and artificial intelligence technologies has accelerated the development of material products. However, decision-making activities in product development are knowledge-intensive and the reusing of knowledge is worthy of studying. In this work, an industrial internet platform is developed to obtain multi-source data from the information systems in steel industry. To mine heterogeneous development knowledge from the tabular data, time series data, pictorial data and textual data, algorithms such as association rule analysis are applied. Metallurgical mechanism knowledge and process knowledge are fused to construct the multimodal knowledge graph through contextual association and ontology modelling. Subgraph matching-based knowledge graph retrieval and reinforcement learning-based knowledge graph reasoning are proposed to efficiently reuse the knowledge in the development of steel products. Practical application validates the effectiveness of the proposed approach in making quick decisions and accelerating the progress of product development.