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- ItemSciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph(Aachen : RWTH, 2020) Anteghini, Marco; D'Souza, Jennifer; Martins dos Santos, Vitor A.P.; Auer, SörenAs a novel contribution to the problem of semantifying bio- logical assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KGs.