Increased access to the radio frequency spectrum is critical to continued wireless innovation, yet there is limited understanding about how this valuable natural resource is being used. Measurements play a key role in understanding spectrum use and can be used to build sophisticated machine learning models for spectrum access and management. Although there have been many different spectrum measurement campaigns, technical and regulatory communities still struggle to extract the information they need from the existing studies and datasets. Spectrum measurements are highly complex spatiotemporal data sets requiring specialized domain knowledge to collect, analyze and interpret. A variety of contextual information ranging from the specific configuration of the spectrum sensor and potential emitters to drivers of spectrum use is needed to understand and document the coverage and limitations of spectrum measurements. This project involves a knowledge graph approach to spectrum explainability. The knowledge graph unifies relevant contextual information from a variety of sources with spectrum measurement summaries.