  A plethora of scientific software packages are published in repositories,
e.g., Zenodo and figshare. These software packages are crucial for the
reproducibility of published research. As an additional route to scholarly
knowledge graph construction, we propose an approach for automated extraction
of machine actionable (structured) scholarly knowledge from published software
packages by static analysis of their (meta)data and contents (in particular
scripts in languages such as Python). The approach can be summarized as
follows. First, we extract metadata information (software description,
programming languages, related references) from software packages by leveraging
the Software Metadata Extraction Framework (SOMEF) and the GitHub API. Second,
we analyze the extracted metadata to find the research articles associated with
the corresponding software repository. Third, for software contained in
published packages, we create and analyze the Abstract Syntax Tree (AST)
representation to extract information about the procedures performed on data.
Fourth, we search the extracted information in the full text of related
articles to constrain the extracted information to scholarly knowledge, i.e.
information published in the scholarly literature. Finally, we publish the
extracted machine actionable scholarly knowledge in the Open Research Knowledge
Graph (ORKG).
