  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.
