Commonsense Knowledge Base Construction in the Age of Big Data
This work addresses the challenge of building commonsense knowledge bases for AI and data management communities, but it is incremental as it builds on existing automated approaches.
The paper tackles the problem of automating commonsense knowledge base construction by showcasing three systems—Quasimodo, Dice, and Ascent—that demonstrate engineering, cleaning, and modeling aspects, with online demos provided.
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.