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2010
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Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia

11 years 3 months ago
Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia
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 of the dynamic nature of knowledge. Categories and Subject Descriptors H.3 [Information Storage and Retrieval] General Terms: Knowledge Management, Ontology
Yafang Wang, Mingjie Zhu, Lizhen Qu, Marc Spaniol,
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2010
Where EDBT
Authors Yafang Wang, Mingjie Zhu, Lizhen Qu, Marc Spaniol, Gerhard Weikum
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