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Title: SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
Authors: Anteghini, MarcoD'Souza, JenniferMartins dos Santos, Vitor A. P.Auer, Sören
Issue Date: 2020
Published in: Proceedings of the EKAW 2020 Posters and Demonstrations Session co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020)
Publisher: Aachen : RWTH
Abstract: As 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.
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Keywords: Open Science Graphs; Bioassays; Machine Learning
DDC: 004
License: CC BY 4.0 Unported
Link to License: https://creativecommons.org/licenses/by/4.0/
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