The Digitalization of Bioassays in the Open Research Knowledge Graph
This tool addresses the problem of inefficient knowledge access for biochemists and pharmaceutical researchers by enabling easier semantification of scholarly contributions, though it is incremental as it builds on existing knowledge graph frameworks.
The paper tackles the challenge of converting unstructured bioassay data into structured, machine-readable knowledge graphs by introducing ORKG-assays, an AI micro-service that achieves competitive performance in semantifying bioassays, as demonstrated on a dataset of 900 bioassays with 5,514 property-value pairs.
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.