ASER: A Large-scale Eventuality Knowledge Graph
This addresses the problem of lacking event-based knowledge for natural language understanding, though it appears incremental as it extends existing knowledge graph approaches to a new type of knowledge.
The researchers tackled the gap in large-scale knowledge graphs that focus on entities but ignore activities, states, and events by developing ASER, a large-scale eventuality knowledge graph extracted from over 11 billion tokens of text, containing 194 million unique eventualities and 64 million unique edges.
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.