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  Commonsense Knowledge Base Construction in the Age of Big Data

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

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arXiv:2105.01925.pdf (Preprint), 106KB
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arXiv:2105.01925.pdf
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File downloaded from arXiv at 2021-10-25 10:51 Manuscript for the cancelled BTW 2021 demo track
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 Creators:
Razniewski, Simon1, Author           
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1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Free keywords: Computer Science, Artificial Intelligence, cs.AI,Computer Science, Computation and Language, cs.CL,Computer Science, Databases, cs.DB
 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.

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Language(s): eng - English
 Dates: 2021-05-052021
 Publication Status: Published online
 Pages: 5 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2105.01925
URI: https://arxiv.org/abs/2105.01925
BibTex Citekey: Razniewski_2105.01925
 Degree: -

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