# The Digitalization of Bioassays in the Open Research Knowledge Graph

arXiv: https://arxiv.org/abs/2203.14574 · HTML: https://mlsanity.com/paper/2203.14574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://mlsanity.com/paper/2106.07385/) (2021)
- [Domain-independent Extraction of Scientific Concepts from Research Articles](https://mlsanity.com/paper/2001.03067/) (2020)
- [UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions](https://mlsanity.com/paper/2105.05435/) (2021)
- [Representing Semantified Biological Assays in the Open Research Knowledge Graph](https://mlsanity.com/paper/2009.07642/) (2020)
- [SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph](https://mlsanity.com/paper/2009.08801/) (2020)
- [Easy Semantification of Bioassays](https://mlsanity.com/paper/2111.15182/) (2021)

## Abstract (arXiv)

Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG) https://www.orkg.org/ represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.