@inproceedings{shen-etal-2018-web,
    title = "A Web-scale system for scientific knowledge exploration",
    author = "Shen, Zhihong  and
      Ma, Hao  and
      Wang, Kuansan",
    editor = "Liu, Fei  and
      Solorio, Thamar",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P18-4015/",
    doi = "10.18653/v1/P18-4015",
    pages = "87--92",
    abstract = "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."
}