  The value of structured scholarly knowledge for research and society at large
is well understood, but producing scholarly knowledge (i.e., knowledge
traditionally published in articles) in structured form remains a challenge. We
propose an approach for automatically extracting scholarly knowledge from
published software packages by static analysis of their metadata and contents
(scripts and data) and populating a scholarly knowledge graph with the
extracted knowledge. Our approach is based on mining scientific software
packages linked to article publications by extracting metadata and analyzing
the Abstract Syntax Tree (AST) of the source code to obtain information about
the used and produced data as well as operations performed on data. The
resulting knowledge graph includes articles, software packages metadata, and
computational techniques applied to input data utilized as materials in
research work. The knowledge graph also includes the results reported as
scholarly knowledge in articles.
