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.
@article{arxiv.1905.00270,
title = {ASER: A Large-scale Eventuality Knowledge Graph},
author = {Hongming Zhang and Xin Liu and Haojie Pan and Yangqiu Song and Cane Wing-Ki Leung},
journal= {arXiv preprint arXiv:1905.00270},
year = {2020}
}