ASER: A Large-scale Eventuality Knowledge Graph

被引：65

作者：

Zhang, Hongming ^{[1
]}

Liu, Xin ^{[1
]}

Pan, Haojie ^{[1
]}

Song, Yangqiu ^{[1
]}

Leung, Cane Wing-Ki ^{[2
]}

机构：

[1] HKUST, CSE, Hong Kong, Peoples R China

[2] Wisers AI Lab, Hong Kong, Peoples R China

来源：

WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020) | 2020年

关键词：

COMMONSENSE;

D O I：

10.1145/3366423.3380107

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：201 / 211

页数：11

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