  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. A web interface, data and code can be found at
https://ascent.mpi-inf.mpg.de/.
