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.

BabelNet is a multilingual semantic network obtained as an integration of WordNet and Wikipedia.
BabelNet
Stable release
BabelNet 4.0 / February 2018
Operating system
Type
LicenseAttribution-NonCommercial-ShareAlike 3.0 Unported
Websitebabelnet.org

Statistics of BabelNet

As of February 2018, BabelNet (version 4.0) covers 284 languages, including all European languages, most Asian languages, and Latin. BabelNet 4.0 contains almost 16 million synsets and about 833 million 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 bilion edges).[1] Version 4.0 also associates about 53 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.

Applications

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:

  • semantic relatedness[4][5]
  • multilingual Word Sense Disambiguation[6]
  • multilingual Word Sense Disambiguation and Entity Linking with the Babelfy system[7]
  • video games with a purpose[8]

Prizes and acknowledgments

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's article[9] about the new age of innovative and up-to-date lexical knowledge resources available on the Web.

gollark: I mean "simple" as in "one recipe for each item, no muultiple-output recipes, no loops", which is quite limiting.
gollark: Very simple autocrafting *is* doable without huge problems - Dragon had an implementation - but that's not very good.
gollark: Are you suggesting we should cover anything but the maximally general case? HERESY!
gollark: In fact, not only is it computationally NP-hard, apparently (Squid did some insanity here: https://squiddev.cc/2018/01/28/ae-sat.html), but to do it *well* you also have to somehow make highly subjective decisions!
gollark: Auto-crafting turns out to be extremely hard.

See also

References
  1. R. Navigli and S. P Ponzetto. 2012. BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network. Artificial Intelligence, 193, Elsevier, pp. 217-250.
  2. R. Navigli, S. P. Ponzetto. BabelNet: Building a Very Large Multilingual Semantic Network. Proc. of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010), Uppsala, Sweden, July 11–16, 2010, pp. 216–225.
  3. M. Ehrmann, F. Cecconi, D. Vannella, J. McCrae, P. Cimiano, R. Navigli. Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0. Proc. of the 9th Language Resources and Evaluation Conference (LREC 2014), Reykjavik, Iceland, 26–31 May 2014.
  4. R. Navigli and S. Ponzetto. 2012. BabelRelate! A Joint Multilingual Approach to Computing Semantic Relatedness. Proc. of the 26th AAAI Conference on Artificial Intelligence (AAAI 2012), Toronto, Canada, pp. 108-114.
  5. J. Camacho-Collados, M. T. Pilehvar and R. Navigli. NASARI: a Novel Approach to a Semantically-Aware Representation of Items. Proc. of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2015), Denver, Colorado (US), 31 May-5 June 2015, pp. 567-577.
  6. R. Navigli and S. Ponzetto. Joining Forces Pays Off: Multilingual Joint Word Sense Disambiguation. Proc. of the 2012 Conference on Empirical Methods in Natural Language Processing (EMNLP 2012), Jeju, Korea, July 12–14, 2012, pp. 1399-1410.
  7. A. Moro, A. Raganato, R. Navigli. Entity Linking meets Word Sense Disambiguation: a Unified Approach Archived 2014-08-08 at the Wayback Machine Transactions of the Association for Computational Linguistics (TACL), 2, pp. 231-244, 2014.
  8. D. Jurgens, R. Navigli. "It's All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation" (PDF). Archived from the original on January 3, 2015. Retrieved 2015-01-03.CS1 maint: BOT: original-url status unknown (link) Transactions of the Association for Computational Linguistics (TACL), 2, pp. 449-464, 2014.
  9. Katy Steinmetz. Redefining the modern dictionary, TIME magazine, vol. 187, 23 May 2016, pp. 20-21.
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