Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
doi:10.24432/C56K5H
TL;DR
We propose a scalable method to construct the large-scale eventuality entailment graph with high precision. Computational and cognitive studies suggest that the abstraction of eventualities (activities, states, and events) is crucial for humans to understand daily eventualities. In this paper, we propose a scalable approach to model the entailment relations between eventualities ("eat an apple'' entails ''eat fruit''). As a result, we construct a large-scale eventuality entailment graph (EEG), which has 10 million eventuality nodes and 103 million entailment edges. Detailed experiments and analysis demonstrate the effectiveness of the proposed approach and quality of the resulting knowledge graph. Our datasets and code are available at https://github.com/HKUST-KnowComp/ASER-EEG.
Citation
@inproceedings{
yu2020enriching,
title={Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations},
author={Changlong Yu and Hongming Zhang and Yangqiu Song and Wilfred Ng and Lifeng Shang},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=-oXaOxy6up},
doi={10.24432/C56K5H}
}
