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:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.5.pdf
Data
ATOMICAscent KBConceptNetQuasimodoWebChild