  Hierarchical domain-specific classification schemas (or subject heading
vocabularies) are often used to identify, classify, and disambiguate concepts
that occur in scholarly articles. In this work, we develop, apply, and evaluate
a human-in-the-loop workflow that first extracts an initial category tree from
crowd-sourced Wikipedia data, and then combines community detection, machine
learning, and hand-crafted heuristics or rules to prune the initial tree. This
work resulted in WikiCSSH; a large-scale, hierarchically organized vocabulary
for the domain of computer science (CS). Our evaluation suggests that WikiCSSH
outperforms alternative CS vocabularies in terms of vocabulary size as well as
the performance of lexicon-based key-phrase extraction from scholarly data.
WikiCSSH can further distinguish between coarse-grained versus fine-grained CS
concepts. The outlined workflow can serve as a template for building
hierarchically-organized subject heading vocabularies for other domains that
are covered in Wikipedia.
