  Knowledge about the software used in scientific investigations is necessary
for different reasons, including provenance of the results, measuring software
impact to attribute developers, and bibliometric software citation analysis in
general. Additionally, providing information about whether and how the software
and the source code are available allows an assessment about the state and role
of open source software in science in general. While such analyses can be done
manually, large scale analyses require the application of automated methods of
information extraction and linking. In this paper, we present SoftwareKG - a
knowledge graph that contains information about software mentions from more
than 51,000 scientific articles from the social sciences. A silver standard
corpus, created by a distant and weak supervision approach, and a gold standard
corpus, created by manual annotation, were used to train an LSTM based neural
network to identify software mentions in scientific articles. The model
achieves a recognition rate of .82 F-score in exact matches. As a result, we
identified more than 133,000 software mentions. For entity disambiguation, we
used the public domain knowledge base DBpedia. Furthermore, we linked the
entities of the knowledge graph to other knowledge bases such as the Microsoft
Academic Knowledge Graph, the Software Ontology, and Wikidata. Finally, we
illustrate, how SoftwareKG can be used to assess the role of software in the
social sciences.
