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 frequency-based baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method.
@article{arxiv.2009.08801,
title = {SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph},
author = {Marco Anteghini and Jennifer D'Souza and Vitor A. P. Martins dos Santos and Sören Auer},
journal= {arXiv preprint arXiv:2009.08801},
year = {2020}
}
Comments
In proceedings of the '22nd International Conference on Knowledge Engineering and Knowledge Management' 'Demo and Poster section'