Commonsense Knowledge Base Construction in the Age of Big Data
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Abstract (arXiv)
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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7 of 14 references.
References · 7
- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
- 2020Machine Knowledge: Creation and Curation of Comprehensive Knowledge Basescited by 55
- 2020TransOMCS: From Linguistic Graphs to Commonsense Knowledgecited by 52
- 2020CSKG: The CommonSense Knowledge Graphcited by 45
- 2019Commonsense Properties from Query Logs and Question Answering Forumscited by 26
- 2020Advanced Semantics for Commonsense Knowledge Extractioncited by 24
- 2019Subjective Databasescited by 7
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