  To enable efficient exploration of Web-scale scientific knowledge, it is
necessary to organize scientific publications into a hierarchical concept
structure. In this work, we present a large-scale system to (1) identify
hundreds of thousands of scientific concepts, (2) tag these identified concepts
to hundreds of millions of scientific publications by leveraging both text and
graph structure, and (3) build a six-level concept hierarchy with a
subsumption-based model. The system builds the most comprehensive cross-domain
scientific concept ontology published to date, with more than 200 thousand
concepts and over one million relationships.
