SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
This work addresses the semantification of biological assays for researchers in bioinformatics, but it is incremental as it builds on existing neural methods.
The paper tackles the problem of automatically structuring unstructured bioassay text descriptions using a neural-network-based approach, achieving a 72% F1 score compared to 47% F1 for a baseline frequency-based method.
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.