UWN: A Large Multilingual Lexical Knowledge Base

•

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We present UWN, a large multilingual lexical knowledge base that describes the meanings and relationships of words in over 200 languages. This paper explains how link prediction, information integration and taxonomy induction methods have been used to build UWN based on WordNet and extend it with millions of named entities from Wikipedia. We additionally introduce extensions to cover lexical relationships, frame-semantic knowledge, and language data. An online interface provides human access to the data, while a software API enables applications to look up over 16 million words and names.

Data & Analytics

Step 1: Link PredictionStep 1: Link Prediction
UWN's Multilingual GraphUWN's Multilingual Graph
• Goal: Richer, Less Sparse Features
• How: Model Synonymy, Polysemy,
Semantic Relatedness, Taxonomy.
(within and across languages)
UWN: A Large Multilingual
Lexical Knowledge Base
Gerard de Melo and Gerhard Weikum
ICSI Berkeley / Max Planck Institute for Informatics
Better NLP Features using Lexical SemanticsBetter NLP Features using Lexical Semantics
More Information:
www.lexvo.org/gdm/
• Downloadable API
available
• Web User Interface
EntityEntitypor: “entidade”por: “entidade”
cmn: “ 制度”cmn: “ 制度” InstitutionInstitution
Educational
institution
Educational
institution
UniversityUniversity
heb: “‫ישות‬.”heb: “‫ישות‬.”
deu: “Bildungs-
einrichtung”
deu: “Bildungs-
einrichtung”
srp:
“универзитете”
srp:
“универзитете”
...
University of
California, Berkeley
University of
California, Berkeley
eng: “Berkeley ”eng: “Berkeley ”
ara:
“‫كينونة‬ ،‫”وجود‬
ara:
“‫كينونة‬ ،‫”وجود‬
tha: “ สถาบัน”tha: “ สถาบัน”
fin: “oppilaitos”fin: “oppilaitos”
fin: “yliopisto”fin: “yliopisto”
cmn:
“ 柏克萊加州大學”
cmn:
“ 柏克萊加州大學”
Berkeley, CABerkeley, CA
George BerkeleyGeorge Berkeley
deu: “Schulgebäude”deu: “Schulgebäude”
school
(group of fish)
school
(group of fish)
school
(institution)
school
(institution)
school
(building)
school
(building)
deu: “Schulhaus”deu: “Schulhaus”
deu: “Fischschwarm”deu: “Fischschwarm”
ces: “hejno”ces: “hejno”
fra: “banc”fra: “banc”
chv: “шкул”chv: “шкул”
jpn: “ 学校”jpn: “ 学校”
kor: “ 학교”kor: “ 학교”
lao: “ໂຮງຮຽນ”lao: “ໂຮງຮຽນ”
kat: “სკოლა”kat: “სკოლა”
• Over 16 million words
and names in over 200
languages semantically
connected
• Ambiguity and
synonymy captured
eng: “UC Berkeley”eng: “UC Berkeley” eng: “Cal”eng: “Cal”
CityCity
Geopolitical
Entity
Geopolitical
Entity
ChuvashChuvash
GeorgianGeorgian
Lexvo.org
Language
Descriptions:
Languages
Scripts
Characters
Countries
Cyrllic
(Script)
Cyrllic
(Script)
Russia
(Country)
Russia
(Country)
UWN: Meaning Distinctions
Ontological
Taxonomy
Encyclopedic
Knowledge,
Pictures,
Video,
Sounds, Maps
Etymological and
other word
relationships
Millions of
Named Entities
(People, Places,
Proteins,
Asteroids,
Companies, etc.)
200+ languages
Step 2: Entity IntegrationStep 2: Entity Integration
Step 3: Taxonomy InductionStep 3: Taxonomy Induction ExtrasExtras
• Markov Chain to rank taxonomic parents
• 270 Wikipedia taxonomies integrated with
WordNet's hypernym hierarchy
es: Televisores: Televisor
es: Televisiónes: Televisión
ru: Телевизорru: Телевизор
hi: दूरदर्शनhi: दूरदर्शन
ja: テレビja: テレビ
en: Televisionen: Television
en:
Television
set
en:
Television
set
zh: 电视机zh: 电视机
ja: テレビ受像機ja: テレビ受像機
en: TV seten: TV set
en: T.V.en: T.V.
V1 ,u
V1 ,u
V1 ,v
V1 ,v
• LP for constraint-based computation of
equivalence classes of entities
• Region Growing approximation algorithm
• Link multilingual
words to WordNet
• Connect Wikipedia
with WordNet
(equivalence and
taxonomic links)
• FrameNet Linking
• Common-Sense
Knowledge Extraction
• Multilingual Roget's
Thesaurus

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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1. Step 1: Link PredictionStep 1: Link Prediction UWN's Multilingual GraphUWN's Multilingual Graph • Goal: Richer, Less Sparse Features • How: Model Synonymy, Polysemy, Semantic Relatedness, Taxonomy. (within and across languages) UWN: A Large Multilingual Lexical Knowledge Base Gerard de Melo and Gerhard Weikum ICSI Berkeley / Max Planck Institute for Informatics Better NLP Features using Lexical SemanticsBetter NLP Features using Lexical Semantics More Information: www.lexvo.org/gdm/ • Downloadable API available • Web User Interface EntityEntitypor: “entidade”por: “entidade” cmn: “ 制度”cmn: “ 制度” InstitutionInstitution Educational institution Educational institution UniversityUniversity heb: “‫ישות‬.”heb: “‫ישות‬.” deu: “Bildungs- einrichtung” deu: “Bildungs- einrichtung” srp: “универзитете” srp: “универзитете” ... University of California, Berkeley University of California, Berkeley eng: “Berkeley ”eng: “Berkeley ” ara: “‫كينونة‬ ،‫”وجود‬ ara: “‫كينونة‬ ،‫”وجود‬ tha: “ สถาบัน”tha: “ สถาบัน” fin: “oppilaitos”fin: “oppilaitos” fin: “yliopisto”fin: “yliopisto” cmn: “ 柏克萊加州大學” cmn: “ 柏克萊加州大學” Berkeley, CABerkeley, CA George BerkeleyGeorge Berkeley deu: “Schulgebäude”deu: “Schulgebäude” school (group of fish) school (group of fish) school (institution) school (institution) school (building) school (building) deu: “Schulhaus”deu: “Schulhaus” deu: “Fischschwarm”deu: “Fischschwarm” ces: “hejno”ces: “hejno” fra: “banc”fra: “banc” chv: “шкул”chv: “шкул” jpn: “ 学校”jpn: “ 学校” kor: “ 학교”kor: “ 학교” lao: “ໂຮງຮຽນ”lao: “ໂຮງຮຽນ” kat: “სკოლა”kat: “სკოლა” • Over 16 million words and names in over 200 languages semantically connected • Ambiguity and synonymy captured eng: “UC Berkeley”eng: “UC Berkeley” eng: “Cal”eng: “Cal” CityCity Geopolitical Entity Geopolitical Entity ChuvashChuvash GeorgianGeorgian Lexvo.org Language Descriptions: Languages Scripts Characters Countries Cyrllic (Script) Cyrllic (Script) Russia (Country) Russia (Country) UWN: Meaning Distinctions Ontological Taxonomy Encyclopedic Knowledge, Pictures, Video, Sounds, Maps Etymological and other word relationships Millions of Named Entities (People, Places, Proteins, Asteroids, Companies, etc.) 200+ languages Step 2: Entity IntegrationStep 2: Entity Integration Step 3: Taxonomy InductionStep 3: Taxonomy Induction ExtrasExtras • Markov Chain to rank taxonomic parents • 270 Wikipedia taxonomies integrated with WordNet's hypernym hierarchy es: Televisores: Televisor es: Televisiónes: Televisión ru: Телевизорru: Телевизор hi: दूरदर्शनhi: दूरदर्शन ja: テレビja: テレビ en: Televisionen: Television en: Television set en: Television set zh: 电视机zh: 电视机 ja: テレビ受像機ja: テレビ受像機 en: TV seten: TV set en: T.V.en: T.V. V1 ,u V1 ,u V1 ,v V1 ,v • LP for constraint-based computation of equivalence classes of entities • Region Growing approximation algorithm • Link multilingual words to WordNet • Connect Wikipedia with WordNet (equivalence and taxonomic links) • FrameNet Linking • Common-Sense Knowledge Extraction • Multilingual Roget's Thesaurus

UWN: A Large Multilingual Lexical Knowledge Base

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