Refractory materialsRefractory material are a crucial part of every aggregate used to produce many materials where temperatures exceed 1,200 °C, such as copperCopper, nickelNickel, steel, cement, lime, and glass. As most refractory materialsRefractory material are manufactured from various raw materials via different process routes, there is a huge variety available. Selecting the most suitable material for each application and furnaceFurnace area is challenging and requires (1) a deep understanding of the dominant wear mechanisms—highly affected by local conditions (e.g. slag chemistrySlag chemistry)—and (2) profound knowledge about refractory materialsRefractory material, including the relevant properties necessary to achieve the expected performancePerformance under process conditions. The large number of refractoryRefractories products available, and the increased generation of public and proprietary know-how on refractoryRefractories products and their properties (e.g. literature or internal R&D reports) turns the material selectionMaterial selection process a task that requires a transformation from an expert-centred towards a data-centered approach. The article proposes an approach to transform the material selectionMaterial selection process to make better informed decisions by combining existing databases with machine learningMachine learning modelsModel and knowledge graphs. In addition to addressing the expected challenges, the article outlines the proposed architecture of a self-learning, interactive refractoryRefractories recommendation system.