English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Advanced Semantics for Commonsense Knowledge Extraction

MPS-Authors

Nguyen,  Tuan-Phong
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons212613

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

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Ressource
No external resources are shared
Fulltext (public)

arXiv:2011.00905.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Nguyen, T.-P., Razniewski, S., & Weikum, G. (2020). Advanced Semantics for Commonsense Knowledge Extraction. WWW 2021. Retrieved from https://arxiv.org/abs/2011.00905.


Cite as: http://hdl.handle.net/21.11116/0000-0007-FEDA-6
Abstract
Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.