论文标题
通过构成关系丰富大规模的最终知识图图
Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
论文作者
论文摘要
计算和认知研究表明，意外（活动，州和事件）的抽象对于人类了解日常情况至关重要。在本文中，我们提出了一种可扩展的方法，以模拟最终之间的需要关系（“吃苹果”需要“吃水果”）。结果，我们构建了一个大规模的最终性图形（EEG），该图具有1000万个最终性节点和100300万个索赔，详细的实验和分析的方法和分析的方法是效率和典型的质量，并且质量是构建的，并且质量质量是质量的，并且质量质量质量是质量的。 https://github.com/hkust-knowcomp/aser-eeg。
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.
