ASER: A Large-scale Eventuality Knowledge Graph

Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, Cane Wing-Ki Leung
5/1/2019
cs.AIcs.CL

Official code: HKUST-KnowComp/ASER (309 stars, MIT, last commit 2024-04-09).

Abstract

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.

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Code Implementations(1)

HKUST-KnowComp/ASEROfficial100%
MIT

ASER (Activities, States, Events, and their Relations): a large-scale weighted eventuality knowledge graph.

30931PythonMay 1, 20192 years agoMIT
asercommonsense-knowledgeknowledge-baseknowledge-graphsemantic-networks

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Cite this paper

@article{zhang2019aser,
  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},
  year   = {2019},
  eprint = {1905.00270},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1905.00270}
}

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