# Representing Semantified Biological Assays in the Open Research Knowledge Graph

arXiv: https://arxiv.org/abs/2009.07642 · HTML: https://mlsanity.com/paper/2009.07642/

## 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)

In the biotechnology and biomedical domains, recent text mining efforts advocate for machine-interpretable, and preferably, semantified, documentation formats of laboratory processes. This includes wet-lab protocols, (in)organic materials synthesis reactions, genetic manipulations and procedures for faster computer-mediated analysis and predictions. Herein, we present our work on the representation of semantified bioassays in the Open Research Knowledge Graph (ORKG). In particular, we describe a semantification system work-in-progress to generate, automatically and quickly, the critical semantified bioassay data mass needed to foster a consistent user audience to adopt the ORKG for recording their bioassays and facilitate the organisation of research, according to FAIR principles.