  Scientific knowledge graphs have been proposed as a solution to structure the
content of research publications in a machine-actionable way and enable more
efficient, computer-assisted workflows for many research activities.
Crowd-sourcing approaches are used frequently to build and maintain such
scientific knowledge graphs. To contribute to scientific knowledge graphs,
researchers need simple and easy-to-use solutions to generate new knowledge
graph elements and establish the practice of semantic representations in
scientific communication. In this paper, we present a workflow for authors of
scientific documents to specify their contributions with a LaTeX package,
called SciKGTeX, and upload them to a scientific knowledge graph. The SciKGTeX
package allows authors of scientific publications to mark the main
contributions of their work directly in LaTeX source files. The package embeds
marked contributions as metadata into the generated PDF document, from where
they can be extracted automatically and imported into a scientific knowledge
graph, such as the ORKG. This workflow is simpler and faster than current
approaches, which make use of external web interfaces for data entry. Our user
evaluation shows that SciKGTeX is easy to use, with a score of 79 out of 100 on
the System Usability Scale, as participants of the study needed only 7 minutes
on average to annotate the main contributions on a sample abstract of a
published paper. Further testing shows that the embedded contributions can be
successfully uploaded to ORKG within ten seconds. SciKGTeX simplifies the
process of manual semantic annotation of research contributions in scientific
articles. Our workflow demonstrates how a scientific knowledge graph can
automatically ingest research contributions from document metadata.
