@inproceedings{425fbdc1af1748bb97e5ee528c5b93ff,
title = "SciBERT-based semantification of bioassays in the open research knowledge graph",
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.",
keywords = "Bioassays, Machine Learning, Open Science Graphs",
author = "Marco Anteghini and Jennifer D'Souza and {Martins Dos Santos}, {Vitor A.P.} and S{\"o}ren Auer",
year = "2020",
month = sep,
day = "17",
language = "English",
volume = "2751",
series = "CEUR Workshop Proceedings",
publisher = "Rheinisch-Westfaelische Technische Hochschule Aachen",
pages = "22--30",
editor = "D. Garijo and A. Lawrynowicz",
booktitle = "EKAW-PD 2020",
note = "22nd International Conference on Knowledge Engineering and Knowledge Management - Posters and Demonstrations Session, EKAW-PD 2020 ; Conference date: 16-09-2020 Through 18-09-2020",
}