Commonsense Knowledge Base Construction in the Age of Big Data

TL;DR

This paper demonstrates three systems that automate the construction of commonsense knowledge bases using web data, showcasing advances in knowledge extraction, cleaning, and conceptual modeling for data management.

Contribution

It introduces three novel systems—Quasimodo, Dice, and Ascent—that address different aspects of automated commonsense knowledge base construction.

Findings

Effective knowledge extraction demonstrated by Quasimodo

Schema constraints improve knowledge cleaning with Dice

Conceptual modeling enhances knowledge representation with Ascent

Abstract

Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific interest to the data management community. (i) We use Quasimodo to illustrate knowledge extraction systems engineering, (ii) Dice to illustrate the role that schema constraints play in cleaning fuzzy commonsense knowledge, and (iii) Ascent to illustrate the relevance of conceptual modelling. The demos are available online at https://quasimodo.r2.enst.fr, https://dice.mpi-inf.mpg.de and ascent.mpi-inf.mpg.de.