THESIS
2022
1 online resource (xii, 68 pages) : color illustrations
Abstract
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. Although recent artificial intelligence has made progress in acquiring and modelling
commonsense, attributed to large neural language models and commonsense knowledge graphs
(CKGs), conceptualization is yet to thoroughly be introduced, 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 possible role of conceptualization in commonsense
reasoning, and formulate a framework to replicate human conceptual induction from acquiring abstract
knowledge about abstract concepts....[
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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. Although recent artificial intelligence has made progress in acquiring and modelling
commonsense, attributed to large neural language models and commonsense knowledge graphs
(CKGs), conceptualization is yet to thoroughly be introduced, 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 possible role of conceptualization in commonsense
reasoning, and formulate a framework to replicate human conceptual induction from acquiring abstract
knowledge about abstract concepts. Aided by the taxonomy Probase, we develop tools for
contextualized conceptualization on ATOMIC, a large-scale human annotated CKG. We annotate
a dataset for the validity of conceptualizations for ATOMIC on both event and triple level, develop
a series of heuristic rules based on linguistic features, and train a set of neural models, so as 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. Furthermore, experiments find directly augmenting data
with abstract triples to be helpful in commonsense modelling.
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