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YAGO2: exploring and querying world knowledge in time, space, context, and many languages
Abstract
We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as first-class citizens. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.
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Research works printed but not linked: 10
- 10.5555/1785162.1785216 · doi:10.5555/1785162.1785216
- Banko M., 2007, IJCAI
- 10.1145/1645953.1646020 · DOI
- 10.1007/978-3-642-13486-9_9 · DOI
- 10.1109/TKDE.2007.34 · DOI · Paper page
- Hoffart J., 2010, Max-Planck-Institut für Informatik
- 10.1007/978-3-642-13486-9_29 · DOI
- Ponzetto S. P., 2007, AAAI
- 10.1145/1242572.1242667 · DOI · Paper page
- 10.1145/1656242.1656245 · DOI
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Metadata
- Authors
- Hoffart, J., Suchanek, F. M., Berberich, K., Lewis-Kelham, E., de Melo, G., & Weikum, G.
- Venue
- Proceedings of the 20th international conference companion on World wide web
- Publisher
- ACM
- Published
- 2011-03-28
- Pages
- 229-232
- OpenAlex
- W2119332773
- Cited by
- 334
- References
- 0
- FWCI
- 33.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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