---
title: "YAGO3: A Knowledge Base from — Mahdisoltani 2015"
description: "Most Wikipedia-derived knowledge bases rely almost entirely on the English edition."
authors: ["Farzaneh Mahdisoltani", "Joanna Biega", "Fabian M. Suchanek"]
year: 2015
venue: "CIDR 2015"
source: "http://cidrdb.org/cidr2015/"
licence: "CC BY 3.0 (CIDR 2015)"
licence_url: "https://creativecommons.org/licenses/by/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mahdisoltani-2015-yago3-knowledge-base-multilingual-wikipedias/"
---
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# YAGO3: A Knowledge Base from Multilingual Wikipedias

**Authors:** Farzaneh Mahdisoltani, Joanna Biega, Fabian M. Suchanek · **Year:** 2015 · **Venue:** CIDR 2015 · **Licence:** [CC BY 3.0 (CIDR 2015)](https://creativecommons.org/licenses/by/3.0/)

**Links:** [Publisher's page](http://cidrdb.org/cidr2015/) · [PDF](http://cidrdb.org/cidr2015/Papers/CIDR15_Paper1.pdf) · [OpenAlex](https://openalex.org/W4300693963)

**Citations:** 127 citations (openalex-snapshot · 2026-10-14) · [108](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Most Wikipedia-derived knowledge bases rely almost entirely on the English edition. This leaves out many entities and facts that appear only in other language editions, often because they are too local for English Wikipedia. YAGO3 extends YAGO by combining ten Wikipedia editions into one consistent knowledge base, rather than producing a separate resource for each language.

The method uses Wikidata to merge equivalent entities across languages. It draws on categories, infobox templates and infobox attributes, and attaches every entity to the English WordNet taxonomy. To map foreign infobox attributes onto YAGO's canonical relations, it compares facts extracted from each language with the existing clean English YAGO and keeps mappings that pass a statistical test based on the Wilson score interval. The approach needs no hand-built similarity functions or trained classifiers.

Attribute mappings reached 95–100% precision across European and non-European languages and different scripts. The added languages contributed about 1m entities and 7m facts. The resulting multilingual, taxonomy-backed resource also proved useful for aligning other knowledge bases such as DBpedia.

## Key points
- The source subset of YAGO (Wikipedia + WordNet) comprised 3.4m entities and 17m facts, organized under a set of 77 relations that were defined by hand. Every foreign attribute is either mapped to one of these relations or dropped.
- Non-English editions add roughly 2.5m ordinary facts, 5m type facts and 1m labels. Together with other sources such as redirects, disambiguation pages and Wikidata labels, YAGO3 reaches about 40m facts and 488,469 classes.
- The default mapping setting is a Wilson score threshold of θ = 4%. Applied off the shelf to German attributes against DBpedia, it gave 95% precision and 81% recall. With a support threshold of 100, results rose to 96% precision and 94% recall.
- Aligning DBpedia relations with YAGO achieved 100% weighted precision and 76% weighted recall. This precision is comparable to PARIS.
- In a Le Monde newspaper analysis, YAGO3 added 824k entity mentions covering 32k new entities. The data is available at yago-knowledge.org.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

Topics: [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · [Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/)

## Cited by

Cited by

108 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 103 not yet.

- [A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
- [Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
- [Cross-lingual Name Tagging and Linking for 282 Languages](https://md.research.elamur.ai/free/en/papers/pan-2017-cross-lingual-name-tagging-linking/)
- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
- [面向链接预测的知识图谱表示学习方法综述](https://md.research.elamur.ai/free/en/papers/du-2023-representation-learning-methods/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{mahdisoltani2015yago3,
  title = {{YAGO3: A Knowledge Base from Multilingual Wikipedias}},
  author = {Mahdisoltani, Farzaneh and Biega, Joanna and Suchanek, Fabian M.},
  booktitle = {CIDR 2015},
  year = {2015},
  url = {http://cidrdb.org/cidr2015/}
}
```

- [← previous Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/)
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- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/)

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