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Media type:
E-Article;
Text
Title:
SciBERT-based semantification of bioassays in the open research knowledge graph
Contributor:
Anteghini, Marco
[Author];
D'Souza, Jennifer
[Author];
Dos Santos, Vitor A.P. Martins
[Author];
Auer, Sören
[Author];
Garijo, Daniel
[Author];
Lawrynowicz, Agnieszka
[Author]
Published:
Aachen, Germany : RWTH Aachen, 2020
Published in:EKAW-PD 2020, posters and demonstrations at EKAW 2020 : proceedings of the EKAW 2020 Posters and Demonstrations session, co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020) ; CEUR Workshop Proceedings ; 2751
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
As a novel contribution to the problem of semantifying biological 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