# SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph

arXiv: https://arxiv.org/abs/2009.08801 · HTML: https://mlsanity.com/paper/2009.08801/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Automated Mining of Leaderboards for Empirical AI Research](https://mlsanity.com/paper/2109.13089/) (2021)
- [Easy Semantification of Bioassays](https://mlsanity.com/paper/2111.15182/) (2021)
- [The Digitalization of Bioassays in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14574/) (2022)

## Abstract (arXiv)

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.