About | BabelNet

BabelNet 5.2 covers 520 languages and is obtained from the automatic integration of:

WordNet, the most popular computational lexicon of English (version 3.0).
Open English WordNet, a fork of the Princeton Wordnet developed under an open source methodology (November 2021 release).
Wikipedia, the largest collaborative multilingual Web encyclopedia (October 2022 dump).
OmegaWiki, a large collaborative multilingual dictionary (January 2017 dump).
Wiktionary, a collaborative project to produce a free-content multilingual dictionary (October 2022 dump).
Wikidata, a free knowledge base that can be read and edited by humans and machines alike (October 2022 dump).
GeoNames, a free geographical database covering all countries and containing over eight million placenames (October 2020 dump).
ImageNet, an image database organized according to the WordNet hierarchy (2011 release).
Open Multilingual WordNet, a collection of wordnets available in different languages (January 2021): Albanet, Arabic WordNet (AWN v2), BulTreeBank WordNet (BTB-WN), Chinese Open WordNet, Chinese WordNet (Taiwan), Croatian WordNet, DanNet, Greek WordNet, FinnWordNet, Hebrew WordNet, IceWordNet, ItalWordNet, Japanese WordNet, Lithuanian WordNet, Multilingual Central Repository, MultiWordNet, Norwegian WordNet, Open Dutch WordNet, OpenWN-PT, Princeton WordNet, Persian WordNet, plWordNet, Romanian WordNet, Slovak WordNet, sloWNet, Swedish (SALDO), Thai WordNet, WOLF (WordNet Libre du Français), WoNeF, WordNet Bahasa.
BabelPic, a large collection of non-concrete pictures.
VerbAtlas, the largest language-independent verb predicate and role resource.
HeTOP Q-Codes, a large multilingual health-related lexicon.
Translations obtained from sense-annotated sentences.

BabelNet is linked to different resources and applications from the Sapienza NLP group:

VerbAtlas: a large multilingual verb predicate and role repository.
InVeRo: intelligible verbs and roles produced by a state-of-the-art neural Semantic Role Labeling system.
Train-O-Matic: the first large scale silver data creation approach to multilingual Word Sense Disambiguation.
MuLaN: silver data creation for Word Sense Disambiguation by means of multilingual label propagation.
OneSeC, SensEmBERT and ARES: latent Transformer-based sense representations which achieve state-of-the-art performance in multilingual Word Sense Disambiguation.
Conception: human-intelligibile multilingual representations of BabelNet synsets.
SyntagNet: a large collection of disambiguated free word associations and collocations.
SyntagRank: a SyntagNet- and BabelNet-based multilingual word sense disambiguation system.
Babelfy: a multilingual disambiguation and entity linking system.
Wikipedia Bitaxonomy: a state-of-the-art taxonomy of Wikipedia pages aligned to a taxonomy of Wikipedia categories.

BabelNet is provided as a stand-alone resource with its Java and Python APIs, a SPARQL endpoint and a Linked Data interface as part of the Linguistic Linked Open Data Cloud (LLOD Cloud) cloud.

10 Years of BabelNet: Roberto Navigli

Workshop on Ten Years of BabelNet and Multilingual Neurosymbolic Natural Language Understanding

Many of the latest technologies exploiting BabelNet are presented in this invited talk at SwissText 2020

eLex 2015 invited talk by Roberto Navigli on BabelNet, Babelfy and Beyond

META prize 2015 awarded to Roberto Navigli and his team for BabelNet

META-NET 2015 prize ceremony in Riga, Latvia

BabelNet is celebrating its 10th anniversary with a 2-day workshop!

Based on an original concept by prof. Roberto Navigli at the Sapienza NLP Group (Sapienza University of Rome), which currently hosts it, it is now engineered and maintained by the Babelscape high-tech NLP company. BabelNet and its API are licensed under the BabelNet Non-Commercial License limited to research institutions.