A Web-scale system for scientific knowledge exploration
Z. Shen, Hao Ma, Kuansan Wang · 2018
To enable efficient exploration of Webscale 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 crossdomain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships.