Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering

Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum


Abstract
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website (https://ascent.mpi-inf.mpg.de) and an introductory video (https://youtu.be/qMkJXqu_Yd4) are both available online.
Anthology ID:
2021.acl-demo.5
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–47
Language:
URL:
https://aclanthology.org/2021.acl-demo.5
DOI:
10.18653/v1/2021.acl-demo.5
Bibkey:
Cite (ACL):
Tuan-Phong Nguyen, Simon Razniewski, and Gerhard Weikum. 2021. Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 40–47, Online. Association for Computational Linguistics.
Cite (Informal):
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering (Nguyen et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.5.pdf
Video:
 https://aclanthology.org/2021.acl-demo.5.mp4
Data
ATOMICAscent KBConceptNetQuasimodoWebChild