Commonsense Knowledge Base Construction in the Age of Big Data

Razniewski, Simon

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Commonsense Knowledge Base Construction in the Age of Big Data

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

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arXiv:2105.01925.pdf
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Razniewski, S. (2021). Commonsense Knowledge Base Construction in the Age of Big Data. Retrieved from https://arxiv.org/abs/2105.01925.

Cite as: https://hdl.handle.net/21.11116/0000-0009-6604-0

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.