A Web-scale system for scientific knowledge exploration

Zhihong Shen, Hao Ma, Kuansan Wang


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.
Anthology ID:
P18-4015
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
87–92
Language:
URL:
https://aclanthology.org/P18-4015
DOI:
10.18653/v1/P18-4015
Bibkey:
Cite (ACL):
Zhihong Shen, Hao Ma, and Kuansan Wang. 2018. A Web-scale system for scientific knowledge exploration. In Proceedings of ACL 2018, System Demonstrations, pages 87–92, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
A Web-scale system for scientific knowledge exploration (Shen et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4015.pdf
Data
Microsoft Academic Graph