Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations

Changlong Yu, Hongming Zhang, Yangqiu Song, Wilfred Ng, Lifeng Shang

Keywords: eventuality knowledge graph, entailment graph, commonsense reasoning

TL;DR: We propose a scalable method to construct the large-scale eventuality entailment graph with high precision.

Subject Areas: Knowledge Representation, Semantic Web and Search

Abstract: 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.

Archival Status: Archival

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2006.11824/code)