  Due to the lack of structure, scholarly knowledge remains hardly accessible
for machines. Scholarly knowledge graphs have been proposed as a solution.
Creating such a knowledge graph requires manual effort and domain experts, and
is therefore time-consuming and cumbersome. In this work, we present a
human-in-the-loop methodology used to build a scholarly knowledge graph
leveraging literature survey articles. Survey articles often contain manually
curated and high-quality tabular information that summarizes findings published
in the scientific literature. Consequently, survey articles are an excellent
resource for generating a scholarly knowledge graph. The presented methodology
consists of five steps, in which tables and references are extracted from PDF
articles, tables are formatted and finally ingested into the knowledge graph.
To evaluate the methodology, 92 survey articles, containing 160 survey tables,
have been imported in the graph. In total, 2,626 papers have been added to the
knowledge graph using the presented methodology. The results demonstrate the
feasibility of our approach, but also indicate that manual effort is required
and thus underscore the important role of human experts.
