This paper outlines a proposal for the use of knowledge graphs for historical German domain adaptation. From the EncycNet project, the encyclopedia-based knowledge graph from the early 20th century was borrowed to examine whether text-based domain adaptation using the source encyclopedia's text or graph-based adaptation produces a better domain-specific model. To evaluate the approach, a novel historical test dataset based on a second encyclopedia of the early 20th century was created. This dataset is categorized by knowledge type (factual, linguistic, lexical) with special attention paid to distinguishing simple and expert knowledge. The main finding is that, surprisingly, simple knowledge has the most potential for improvement, whereas expert knowledge lags behind. In this study, broad signals like simple definitions and word origin yielded the best results, while more specialized knowledge such as synonyms were not as effectively represented. A follow-up study was carried out in favor of simple contemporary lexical knowledge to control for historicity and text genre, where the results confirm that language models can still be enhanced by incorporating simple lexical knowledge using the proposed workflow.