ASER: A Large-scale Eventuality Knowledge Graph
| Autor: | Cane Wing-Ki Leung, Haojie Pan, Hongming Zhang, Xin Liu, Yangqiu Song |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Relation (database) Computer science Computer Science - Artificial Intelligence media_common.quotation_subject 02 engineering and technology Data science Focus (linguistics) Artificial Intelligence (cs.AI) Knowledge graph 020204 information systems Scale (social sciences) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Computation and Language (cs.CL) media_common |
| Zdroj: | WWW |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|