Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize.For someone who barely knows about meditation, while is knowledgeable about singing, he can still infer that meditation makes people relaxed from the existing knowledge that singing makes people relaxed by first conceptualizing singing as a relaxing event and then instantiating that event to meditation.This process, known as conceptual induction and deduction, is fundamental to commonsense reasoning while lacking both labeled data and methodologies to enhance commonsense modeling.To fill such a research gap, we propose CAT (Contextualized ConceptuAlization and InsTantiation), a semi-supervised learning framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale.Extensive experiments show that our framework achieves state-of-the-art performances on two conceptualization tasks, and the acquired abstract commonsense knowledge can significantly improve commonsense inference modeling.Our code, data, and fine-tuned models are publicly available at https://github.com/HKUST- KnowComp/CAT. * Equal ContributionPersonX watches football game, as a result, PersonX will: feel relaxed PersonX plays with his dog, as a result, PersonX will: be happy and relaxed PersonX [observe] as a result, PersonX will: feel relaxed PersonX [relaxing event] as a result, PersonX will: feel relaxed Conceptualization (watches football game → relaxing event) Instantiation (relaxing event → plays with his dog)