  Conceptualization, or viewing entities and situations as instances of
abstract concepts in mind and making inferences based on that, is a vital
component in human intelligence for commonsense reasoning. Despite recent
progress in artificial intelligence to acquire and model commonsense attributed
to neural language models and commonsense knowledge graphs (CKGs),
conceptualization is yet to be introduced thoroughly, making current approaches
ineffective to cover knowledge about countless diverse entities and situations
in the real world.
  To address the problem, we thoroughly study the role of conceptualization in
commonsense reasoning, and formulate a framework to replicate human conceptual
induction by acquiring abstract knowledge about events regarding abstract
concepts, as well as higher-level triples or inferences upon them. We then
apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the
taxonomy Probase. We annotate a dataset on the validity of contextualized
conceptualizations from ATOMIC on both event and triple levels, develop a
series of heuristic rules based on linguistic features, and train a set of
neural models to generate and verify abstract knowledge. Based on these
components, a pipeline to acquire abstract knowledge is built. A large abstract
CKG upon ATOMIC is then induced, ready to be instantiated to infer about unseen
entities or situations. Finally, we empirically show the benefits of augmenting
CKGs with abstract knowledge in downstream tasks like commonsense inference and
zero-shot commonsense QA.
