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Paper

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

MPS-Authors
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Nguyen,  Tuan-Phong
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Razniewski,  Simon
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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https://youtu.be/qMkJXqu_Yd4
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arXiv:2105.13662.pdf
(Preprint), 775KB

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Citation

Nguyen, T.-P., Razniewski, S., & Weikum, G. (2021). Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering. Retrieved from https://arxiv.org/abs/2105.13662.


Cite as: http://hdl.handle.net/21.11116/0000-0009-4A2E-2
Abstract
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 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 and an introductory video are both available online.