Roberto Navigli - The BabelNet Project Workshop 16 June 2016 Auditorium Antonianum – Sala San Francesco Viale Manzoni, 1 Roma

1. Roberto Navigli
BabelNet and beyond: a huge multilingual
semantic network and its potential for
interconnecting migration routes
16th June 2016 – Rome
http://lcl.uniroma1.it

2. Roberto Navigli
20/06/2016 2
• Associate Professor in the Department of Computer
Science (Sapienza, Rome)
• Principal investigator of several projects:
– ERC Starting Grant (MultiJEDI)
– FP7 CSA (LIDER)
– Google Focused Research Award (co-PI)
• Managing a team of 10 researchers, out of which 6
Ph.D. students
BabelNet, Babelfy and Beyond!
Roberto Navigli

3. Outline of the talk
• BabelNet: a huge multilingual semantic network
• Babelfy: a state-of-the-art multilingual disambiguation
system
• What's next: how to help interconnect/detect migration
flows with the aid of our technologies
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4. INTEGRATING
KNOWLEDGE
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The resource diaspora

6. • There are many online dictionaries and encyclopedias
• Each covers one or a limited number of languages
• The knowledge found in different resources is often
complementary
– To get coverage of more languages
– To get additional information about the entry
– To obtain links to geographical information
• However, each resource provides different meaning
inventories
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The resource diaspora

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Key Objective 1: create knowledge for all languages
BabelNet: Unifying Lexical Knowledge Resources into
a Single Semantic Network
WordNet
MultiWordNet
WOLF
MCR
GermaNet
BalkaNet
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8. {wheeled vehicle}
{self-propelled vehicle}
{motor vehicle} {tractor}
{car,auto, automobile,
machine, motorcar}
{convertible}
{air bag}
is-a
is-a
is-a
is-a
is-a
has-part{golf cart,
golfcart}
is-a
{wagon,
waggon}
is-a
{accelerator,
accelerator pedal,
gas pedal, throttle}
has-part
{car window}
has-part
{locomotive, engine,
locomotive engine,
railway locomotive}
is-a
{brake}has-part
{wheel}
has-part
{splasher}
has-part
concepts
semantic relation
WordNet [Miller et al., 1990; Fellbaum, 1998]
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9. BabelNet, Babelfy, Video games with a purpose & the Wikipedia Bitaxonomy
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• Playing with senses
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
• Bla bla bla bla bla bla bla
concepts
(unspecified) semantic relation
Wikipedia [The Web Community, 2001-today]

10. • We collect lexicalizations, definitions, translations,
images, etc. from each of the merged resources
Merging entries from different resources into BabelNet
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WordNet

11. BabelNet: concepts and semantic relations
• We encode knowledge as a labeled directed graph:
– Each vertex is a Babel synset (=synonym set)
– Each edge is a semantic relation between synsets:
• is-a (balloon is-a aircraft)
• part-of (gasbag part-of balloon)
• instance-of (Einstein instance-of physicist)
• …
• unspecified/relatedness (balloon related-to flight)
balloonEN, BallonDE,
aerostatoES, aerostatoIT,
pallone aerostaticoIT,
mongolfièreFR
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12. What is BabelNet?
• A merger of resources of different kinds:
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• A merger of resources of different kinds:
– WordNet: the most popular computational lexicon of English
– Open Multilingual WordNet: a collection of open wordnets
– WoNeF: a French WordNet
– Wikipedia: the largest collaborative encyclopedia
– Wikidata: the largest collaborative knowledge base
– Wiktionary: the largest collaborative dictionary
– OmegaWiki: a medium-size collaborative multilingual dictionary
– GeoNames: a worldwide geographical database
– Microsoft Terminology: a computer science thesaurus
– High-quality automatic sense-based translations
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What is BabelNet?

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What is BabelNet?
• A merger of resources of different kinds:
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Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
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Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
– 6M concepts and 7.7M named entities
– 119M word senses
– 378M semantic relations (27 relations per concept on avg.)
– 11M images associated with concepts
– 41M textual definitions
– 2M concepts with domains associated
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17. 20/06/2016Multilingual Web Access – WWW 2015
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Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
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18. 20/06/2016Multilingual Web Access – WWW 2015
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Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
• "Dictionary of the future": semantic network structure
with labeled relations, pictures, multilingual synsets
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Why do we need BabelNet?
• Multilinguality: the same concept is expressed in tens of
languages
• Coverage: 271 languages and 14 million entries!
• Concepts and named entities together: dictionary and
encyclopedic knowledge is semantically interconnected
• "Dictionary of the future": semantic network structure
with labeled relations, pictures, multilingual synsets
• Media coverage and prestigious prizes
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20. ADDRESSING
LEXICAL
AMBIGUITY
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21. Lexical ambiguity!
• Thomas and Mario played as strikers in Munich.
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22. Word Sense Disambiguation and Entity Linking
• Thomas and Mario are strikers playing in Munich
Entity Linking: The task
of discovering mentions
of entities within a text
and linking them in a
knowledge base.
WSD: The task aimed at
assigning meanings to word
occurrences within text.
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23. Multilingual Joint Word Sense Disambiguation
(MultiJEDI)
Key Objective 2: use all languages to disambiguate one
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24. So what?
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25. Step 1: Find all possible meanings of words
“Thomas and Mario are strikers playing in Munich”
Thomas (novel)
Seth Thomas
Thomas Müller
Mario Gómez
Mario (Album)
Mario (Character)
Striker (Movie)
Striker (Video Game)
striker (Sport)
Munich (City)
FC Bayern Munich
Munich (Song)
Ambiguity!
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26. Step 2: Connect all the candidate meanings
Thomas and Mario are strikers playing in Munich
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27. Step 3: Extract a dense subgraph
Thomas and Mario are strikers playing in Munich
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28. Step 3: Extract a dense subgraph
Thomas and Mario are strikers playing in Munich
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29. Step 4: Select the most reliable meanings
“Thomas and Mario are strikers playing in Munich”
Thomas (novel)
Seth Thomas
Thomas Müller
Mario Gómez
Mario (Album)
Mario (Character)
Striker (Movie)
Striker (Video Game)
striker (Sport)
Munich (City)
FC Bayern Munich
Munich (Song)
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31. Experimental Results:
Fine-grained (Multilingual) Disambiguation
Senseval-3
SemEval-2007
task 17
SemEval-2013 task 12
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32. The Crazy Polyglot!
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33. Live demo – Crazy polyglot!
EN In todayʼs knowledge and information society
FR le paysage lexicographique est plus hétérogène que
jamais.
IT Possono le risorse stand-alone competere
ES con múltiples funciones, portale lexicográficas
multilingüe y servicios web,
ZH Web服务，定 制 的 喜 好 和 个 人 用 户 的 个 人 资 料 ？
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34. 1) Geographical named entities are interlinked
• Each geographical entity comes with:
– geolocation information
– translations in dozens of languages
– connections to other concepts and named entities (e.g.
politicians, important places, concepts, events, etc.)
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35. 2) Named entities, events and actions are expressed in
any language
• We can process tweets, facebook/instagram/blog posts
and identify these entities and interconnect them
independently of the language they are expressed in
Οδεύουμε προς τη #Μακεδονία (We are moving to #Macedonia)
Greek police started phase 2 of #Idomeni evacuation, emptying
camp near Polykastro-1,828 people 2B moved
‫إخالء‬!‫الغاز‬ ‫محطة‬ ‫الشغب‬ ‫مكافحة‬ ‫شرطة‬ ‫حاصرت‬ EKO! ‫تركه‬ ‫رافضا‬ ‫الالجئين‬!‫إشعار‬ ‫أي‬‫مسبق‬
(EKO Evacuation! Riot police have surrounded EKO gas station!
Refugees refusing to leave! No prior notice given)
‫نقل‬ ‫سيتم‬‫سكان‬ Idomeni ‫سالونيك‬ ،‫مدينة‬ ‫أكبر‬ ‫ثاني‬ ‫في‬ ‫ذلك‬ ‫في‬ ‫بما‬ ،‫جديدة‬ ‫مخيمات‬ ‫إلى‬.
(Idomeni residents will be moved to new camps, including in the
second-largest city, Thessaloniki.)
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36. 2) Named entities , events and actions are expressed
in any language
• We can process tweets, facebook/instagram/blog posts
and identify these entities and interconnect them
independently of the language they are expressed in
Οδεύουμε προς τη #Μακεδονία (We are moving to #Macedonia)
Greek police started phase 2 of #Idomeni evacuation, emptying
camp near Polykastro-1,828 people 2B moved
‫إخالء‬!‫الغاز‬ ‫محطة‬ ‫الشغب‬ ‫مكافحة‬ ‫شرطة‬ ‫حاصرت‬ EKO! ‫تركه‬ ‫رافضا‬ ‫الالجئين‬!‫إشعار‬ ‫أي‬‫مسبق‬
(EKO Evacuation! Riot police have surrounded EKO gas station!
Refugees refusing to leave! No prior notice given)
‫نقل‬ ‫سيتم‬‫سكان‬ Idomeni ‫سالونيك‬ ،‫مدينة‬ ‫أكبر‬ ‫ثاني‬ ‫في‬ ‫ذلك‬ ‫في‬ ‫بما‬ ،‫جديدة‬ ‫مخيمات‬ ‫إلى‬.
(Idomeni residents will be moved to new camps, including in the
second-largest city, Thessaloniki.)
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37. 3) Predicting where the migration flows are moving next
• Intentions can be automatically identified and
extracted from text
– Including the next most popular actions and events (e.g.
moving, evacuating, going back, etc.)
• Integrated with GPS and satellite views of the places
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38. Summarizing…
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+ helping detect migration flows with our technologies

39. Roberto Navigli
Linguistic Computing Laboratory
http://lcl.uniroma1.it
@RNavigli