Timely YAGO: Harvesting, Querying, and Visualizing Temporal Knowledge from Wikipedia
Authors
- Yafang Wang (Max-Planck Institute for Informatics, Germany)
- mingjie zhu (Max-Planck Institute for Informatics, Germany)
- Lizhen Qu (Max-Planck Institute for Informatics, Germany)
- Marc Spaniol (Max-Planck-Institut for Informatics, Germany)
- Gerhard Weikum (Max Planck Institute for Informatics, Germany)
Abstract
Recent progress in information extraction has shown how to automatically build large ontologies from high-quality sources like Wikipedia. But knowledge evolves over time; facts have associated validity intervals. Therefore, ontologies should include time as a first-class dimension. In this paper, we introduce Timely YAGO, which extends our previously built knowledge base YAGO with temporal aspects. This prototype system extracts temporal facts from Wikipedia infoboxes, categories, and lists in articles, and integrates these into the Timely YAGO knowledge base. We also support querying temporal facts, by temporal predicates in a SPARQL-style language. Visualization of query results is provided in order to better understand the dynamic nature of knowledge.
Session
EDBT Demo Session 1: Demonstrations (Wednesday, March 24, 14:00—15:30)