CC BY 4.0 UnportedAnteghini, MarcoD'Souza, JenniferMartins dos Santos, Vitor A.P.Auer, Sören2021-04-132021-04-132020https://oa.tib.eu/renate/handle/123456789/6144https://doi.org/10.34657/5192As 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.enghttps://creativecommons.org/licenses/by/4.0/004Open Science GraphsBioassaysMachine LearningBookPartKonferenzschrift