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

Source: [Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering](<https://www.sota2.com/research/paper/68fa6d6c1e209131cea39bee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-24T17:31:57.820Z

Authors: Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum

Published: 2021-05-28T08:17:33.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2105.13662>)

[PDF](<https://arxiv.org/pdf/2105.13662.pdf>)

[Implementation](<https://ascent.mpi-inf.mpg.de/>)