When designing materials and components, a multitude of influence factors and process parameters distributed over lengthy process chains sensitively impact the resultant properties and functionality. Effectively, this culminates in a vast design space. To get a grip on it, the knowledge and information about compositionprocess-microstructure-property (CPMP) relationships lying dormant within scientific literature should be utilized better, starting with their extraction into structured semantic representations. The use case of this study is the subdomain of materials fatigue teroperable. This is challenging since data reported in scholarly articles is not only very heterogeneous but also often incomplete and sometimes ambiguous. This is owed to the fact that authors are primarily aiming to publish small excerpts of the underlying experimental work to underpin specific novel findings rather than comprehensive datasets . However, most aforementioned knowledge graph generation approaches did not sufficiently account for the data heterogeneity and ambiguity that can be present in articles. Instead, the algorithms and models can decide on arbitrary entity types and relationships that are neither explicitly defined nor conformant to a standard, which limits the interoperability of derived knowledge graphs with other datasets.