YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames

By Thomas Rebele, Fabian M. Suchanek, Johannes Hoffart, Joanna Asia Biega, Erdal Kuzey, Gerhard Weikum. International Workshop on the Semantic Web, 2016

310 citations recorded by the source corpus.

YAGO is a large knowledge base that is built automatically from Wikipedia, WordNet and GeoNames. The project combines information from Wikipedias in 10 different languages into a coherent whole, thus giving the knowledge a multilingual dimension. It also attaches spatial and temporal information to many facts, and thus allows the user to query the data over space and time. YAGO focuses on extraction quality and achieves a manually evaluated precision of 95 %. In this paper, we explain how YAGO is built from its sources, how its quality is evaluated, how a user can access it, and how other projects utilize it.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/772f69b1dd966610edaa4ef2576d36142dc05928

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tat-seng Chua
2. Jimmy J. Lin
3. Peng Shi
4. Yixin Cao
5. Zhiyuan Liu
6. Juan-Zi Li
7. Xiangnan He
8. Wei Dang Lu

In Information Systems

1. Yixin Cao
2. Jimmy J. Lin
3. Tat-seng Chua
4. Peng Shi
5. Zhiyuan Liu
6. Xu Sun
7. Wei Dang Lu
8. Yanyan Zou

Citations in the corpus

• Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks (2018, background): “Multilingual KGs such as DB-pedia (Bizer et al., 2009), YAGO (Suchanek et al., 2008; Rebele et al., 2016), and BabelNet (Nav-igli and Ponzetto, 2012) are especially valuable if cross-lingual applications are to be built.”
• Aligning Cross-Lingual Entities with Multi-Aspect Information (2019, background): “A growing number of multilingual knowledge graphs (KGs) have been built, such as DBpedia (Bizer et al., 2009), YAGO (Suchanek et al., 2008; Rebele et al., 2016), and BabelNet (Navigli and Ponzetto, 2012), which typically represent realworld knowledge as separately-structured monolingual KGs.”
• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, unknown): “To make a fair and realistic comparison, we construct a set of dedicated benchmark datasets with splits of five folds by sampling real-world KGs DBpedia [43], Wikidata [86] and YAGO [70], in consideration of various aspects of heterogeneity regarding entity degrees, multilingualism, schemata and sca”
• Multi-Channel Graph Neural Network for Entity Alignment (2019, background): “Error Analysis : As shown in Table 2, the only 4 rules transfer from YAGO3 to DBpedia are grounded to 92,923 new ground rule triples, which is shocking and not informative.”