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

TinyGenius: Intertwining Natural Language Processing with Microtask Crowdsourcing for Scholarly Knowledge Graph Creation