Synthetic rubber polymers are macromolecules designed for a wide area of applications ranging from car tires and sealings to cables and adhesives. Due to complexity of their chemical and physical structure, the precise measurement of the polymer properties is essential for manufacturing high-performance rubbers. Moreover, the tracking and understanding of the effect of these properties on the mixing process and the properties of the final compound is of utmost importance for the development of material models. Such optimized models can accelerate the digitalization of polymer production, which is one of the goals of the InSuKa project. Optimized digital models enable predictive optimization strategies for process parameter and recipe adjustments. To support this, we developed the DIGIT RUBBER ontology, which formalizes empirical knowledge of polymer formulations and processing. DIGIT RUBBER, created within the Platform Material Digital (PMD) initiative, focuses on polymer mixing and extrusion and is aligned with the PMDCo3, a mid-level domain ontology for materials science and engineering. PMD also provides graph patterns, reusable structural templates for representing knowledge in Web Ontology Language (OWL) based graphs. The project involves building a pipeline for the consistent transformation of raw polymer datasets (and their compounds) into knowledge graphs. In this work, which is embedded in the project InSuKa, we apply the measurement graph patterns using LinkML to transform raw polymer and compound datasets stored in relational databases into OWL knowledge graphs. The pipeline also has a graphical user interface that enables any domain expert to query the knowledge graph - PolymerNexus, without a background knowledge of the structure of the graph or the SPARQL query language. The resulting polymer knowledge graphs provide a structured, interoperable representation of data for effective analysis of empirical knowledge inherent in the polymer datasets and supporting the development of optimized material models.