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A critical task for researchers is to find promising future directions and interesting scientific challenges in the literature. To tackle this problem, we hypothesize that structured representations of information in the literature can be used to identify these elements. Specifically, we look at structured representations in the form of Knowledge Graphs (KGs) and we investigate how using different input schemas for extraction impacts the performance on the tasks of classifying sentences as future directions. Our results show that the <jats:sc>MECHANIC-Granular<\/jats:sc> schema yields the best performance across different settings and achieves state of the art performance when combined with pretrained embeddings. 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