  As the number of published scholarly articles grows steadily each year, new
methods are needed to organize scholarly knowledge so that it can be more
efficiently discovered and used. Natural Language Processing (NLP) techniques
are able to autonomously process scholarly articles at scale and to create
machine readable representations of the article content. However, autonomous
NLP methods are by far not sufficiently accurate to create a high-quality
knowledge graph. Yet quality is crucial for the graph to be useful in practice.
We present TinyGenius, a methodology to validate NLP-extracted scholarly
knowledge statements using microtasks performed with crowdsourcing. The
scholarly context in which the crowd workers operate has multiple challenges.
The explainability of the employed NLP methods is crucial to provide context in
order to support the decision process of crowd workers. We employed TinyGenius
to populate a paper-centric knowledge graph, using five distinct NLP methods.
In the end, the resulting knowledge graph serves as a digital library for
scholarly articles.
