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

• URL: http://arxiv.org/abs/2105.13662v1
• Date: Fri, 28 May 2021 08:17:33 GMT
• Title: Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering
• Authors: Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
• Abstract summary: ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact on the use case of question answering.
• Score: 25.385862319865335
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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