# Easy Semantification of Bioassays

arXiv: https://arxiv.org/abs/2111.15182 · HTML: https://mlsanity.com/paper/2111.15182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures](https://mlsanity.com/paper/1905.06939/) (2019)
- [An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols](https://mlsanity.com/paper/1805.00195/) (2018)
- [SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://mlsanity.com/paper/2106.07385/) (2021)
- [Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature](https://mlsanity.com/paper/2002.07339/) (2020)
- [Domain-independent Extraction of Scientific Concepts from Research Articles](https://mlsanity.com/paper/2001.03067/) (2020)
- [The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources](https://mlsanity.com/paper/2003.01006/) (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)

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

- [ORKG-Leaderboards: A Systematic Workflow for Mining Leaderboards as a Knowledge Graph](https://mlsanity.com/paper/2305.11068/) (2023)
- [Clustering Semantic Predicates in the Open Research Knowledge Graph](https://mlsanity.com/paper/2210.02034/) (2022)
- [The Digitalization of Bioassays in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14574/) (2022)

## Abstract (arXiv)

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.