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| Stable release | BabelNet 5.0 / February 2021 |
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| License | Attribution-NonCommercial-ShareAlike 3.0 Unported |
| Website | babelnet |
BabelNet is a multilingual lexicalized semantic network and ontology developed at the NLP group of the Sapienza University of Rome.[1][2] BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. For each Babel synset, BabelNet provides short definitions (called glosses) in many languages harvested from both WordNet and Wikipedia.
As of April 2021[update], BabelNet (version 5.0) covers 500 languages. It contains almost 20 million synsets and around 1.4 billion word senses (regardless of their language). Each Babel synset contains 2 synonyms per language, i.e., word senses, on average. The semantic network includes all the lexico-semantic relations from WordNet (hypernymy and hyponymy, meronymy and holonymy, antonymy and synonymy, etc., totaling around 364,000 relation edges) as well as an underspecified relatedness relation from Wikipedia (totaling around 1.3 billion edges).[1] Version 5.0 also associates around 51 million images with Babel synsets and provides a Lemon RDF encoding of the resource,[3] available via a SPARQL endpoint. 2.67 million synsets are assigned domain labels.
BabelNet has been shown to enable multilingual Natural Language Processing applications. The lexicalized knowledge available in BabelNet has been shown to obtain state-of-the-art results in:
BabelNet received the META prize 2015 for "groundbreaking work in overcoming language barriers through a multilingual lexicalised semantic network and ontology making use of heterogeneous data sources".
BabelNet featured prominently in a Time magazine article[9] about the new age of innovative and up-to-date lexical knowledge resources available on the Web.
{{cite web}}: CS1 maint: bot: original URL status unknown (link) Transactions of the Association for Computational Linguistics (TACL), 2, pp. 449-464, 2014.
