  Understanding human's language requires complex world knowledge. However,
existing large-scale knowledge graphs mainly focus on knowledge about entities
while ignoring knowledge about activities, states, or events, which are used to
describe how entities or things act in the real world. To fill this gap, we
develop ASER (activities, states, events, and their relations), a large-scale
eventuality knowledge graph extracted from more than 11-billion-token
unstructured textual data. ASER contains 15 relation types belonging to five
categories, 194-million unique eventualities, and 64-million unique edges among
them. Both intrinsic and extrinsic evaluations demonstrate the quality and
effectiveness of ASER.
