The Digitalization of Bioassays in the Open Research Knowledge Graph

TL;DR

This paper introduces ORKG-assays, an AI tool that automates the semantification of bioassays into machine-readable triples, enhancing data accessibility for scientists in drug development.

Contribution

The paper presents a novel AI micro-service, ORKG-assays, that efficiently converts bioassay data into structured semantic triples, improving scholarly knowledge representation.

Findings

Competitive performance on gold-standard bioassay data

Enables survey and visualization of assay properties

Facilitates smart knowledge access for biochemists

Abstract

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.