YAGO

YAGO is a large semantic knowledge base, derived from Wikipedia, WordNet, WikiData, GeoNames, and other data sources. Currently, YAGO knows more than 17 million entities (like persons, organizations, cities, etc.) and contains more than 150 million facts about these entities. The accuracy of YAGO has been manually evaluated, proving a confirmed accuracy of 95% ( ). Every relation is annotated with its confidence value. YAGO combines the clean taxonomy of WordNet with the richness of the Wikipedia category system, assigning the entities to more than 350,000 classes. YAGO is anchored in time and space. YAGO attaches a temporal dimension and a spatial dimension to many of its facts and entities. In addition to taxonomy, YAGO has thematic domains such as "music" or "science" from WordNet Domains. YAGO extracts and combines entities and facts from 10 Wikipedias in different languages. YAGO is jointly developed at the DBWeb group at Télécom ParisTech University, the Databases and Information Systems group at the Max Planck Institute for Informatics, and Ambiverse. ( ) Not every version of YAGO is manually evaluated. Most notably, the version generated by this code may not be the one that we evaluated! Check the versions on the YAGO download page If you are just interested in the data of YAGO, there is no need to use the present code repository. You can download data of YAGO from the YAGO homepage. If you run the code yourself, you can define (a) what Wikipedia languages to cover, and (b) which specific Wikipedia, Wikidata, and Wikimedia Commons snapshots should be used during the build. YAGO is configured with a configuration file. Use this template to generate your own copy of that file. It should contain the following lines: This code will create a new configuration file, which you will have to use in the sequel. Alternatively, you can download the required data sources manually. Then add the following lines to your configuration file: Watch out to use the new configuration file if you used the Python script to download the data resources. Allocating 220G of main memory to YAGO is a reasonable estimate which typically works fine, but of course this highly depends on the number of languages you execute the build for. Increase this value if necessary. Once the processing finished, all output can be found in the directory given by the yagoFolder parameter in your configuration file. The YAGO data is split into "themes". Each theme corresponds to a file on disk. A theme contains facts (either in RDF or in TSV, see the section on data formats below). Themes can overlap, but should not. The class basics.Theme implements a theme. Themes that are free of duplicates and ready for export are called "final themes". These live in the same folder as the other themes, but start with yago... . The final themes make up the YAGO knowledge base. Extractors implement extractors.Extractor . Common postprocessing steps (such as translating entities) implement the class FollowUpExtractor . This defines a dependency graph of extractors. Extractors are scheduled in the right order and called by main.ParallelCaller . Facts can have a meta-fact extractionSource . This meta-fact can have a meta-fact extractionTechnique . There should be a finite set of techniques that does not grow with the data. deduplicators: extractors aggregating results from previous ones, removing duplicate facts extractors: abstract classes specifying the interfaces for extractors followUp: classes implementing filtering and mapping postprocessing steps fromGeonames: extractors working on Geonames fromOtherSources: extractors working on Wordnet, Wikidata, etc. fromThemes: extractors depending on other extractors fromWikipedia: extractors working on Wikipedia dumps main: Contains the scheduler that starts the extractors Entity names follow the RDF/Turtle convention. Turtle leaves some design choices open. We use the following conventions: 1. YAGO entities are always gi