the future of knowledge graphs moving beyond simple ...qngw2014.bj.bcebos.com/upload/kg3/kg 2015 -...
TRANSCRIPT
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The Future of Knowledge Graphs:Moving beyond
Simple Collections of Facts
Gerard de MeloAssistant Professor, Tsinghua University
http://gerard.demelo.org
The Future of Knowledge Graphs:Moving beyond
Simple Collections of Facts
Gerard de MeloAssistant Professor, Tsinghua University
http://gerard.demelo.org
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25 Years of the World Wide Web:1989−2014
25 Years of the World Wide Web:1989−2014
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
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25 Years of the World Wide Web:1989−2014
25 Years of the World Wide Web:1989−2014
HyperText(the “HT” in
“HTML”)
HyperText(the “HT” in
“HTML”)
Basic Idea:Connecting Data
Basic Idea:Connecting Data
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
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25 Years of the World Wide Web:1989−2014
25 Years of the World Wide Web:1989−2014
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
![Page 5: The Future of Knowledge Graphs Moving beyond Simple ...qngw2014.bj.bcebos.com/upload/kg3/KG 2015 - The... · Linked Data Is Still Not ... Bordes & Gabrilovich. KDD 2014 Tutorial](https://reader034.vdocument.in/reader034/viewer/2022051803/5b05a4da7f8b9a5c308bca3e/html5/thumbnails/5.jpg)
25 Years of the World Wide Web:1989−2014
25 Years of the World Wide Web:1989−2014
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
![Page 6: The Future of Knowledge Graphs Moving beyond Simple ...qngw2014.bj.bcebos.com/upload/kg3/KG 2015 - The... · Linked Data Is Still Not ... Bordes & Gabrilovich. KDD 2014 Tutorial](https://reader034.vdocument.in/reader034/viewer/2022051803/5b05a4da7f8b9a5c308bca3e/html5/thumbnails/6.jpg)
The Semantic WebThe Semantic Web
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
col-league
born in Frankfurt
describedby
created by
A Web ofmachine-readableentity-relationshipdata
A Web ofmachine-readableentity-relationshipdata
createdby
Frankfurt image: https://en.wikipedia.org/wiki/File:Frankfurt_Am_Main-Stadtansicht_von_der_Deutschherrnbruecke_am_fruehen_Abend-20110808.jpg
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The Semantic WebThe Semantic Web
http://geekcom.wordpress.com/2009/03/19/
Tim Berners-Lee
A Web ofmachine-readableentity-relationshipdata
A Web ofmachine-readableentity-relationshipdata
Frankfurt image: https://en.wikipedia.org/wiki/File:Frankfurt_Am_Main-Stadtansicht_von_der_Deutschherrnbruecke_am_fruehen_Abend-20110808.jpg
Problem:Just an academicidea, not used inpractice!
Problem:Just an academicidea, not used inpractice!
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Big Knowledge CollectionsBig Knowledge Collections
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Big Knowledge CollectionsBig Knowledge CollectionsBig Knowledge CollectionsBig Knowledge Collections
https://commons.wikimedia.org/wiki/File:Encyclopedia_Britannica_in_the_library_of_The_Kings_School,_Goa.jpg
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Big Knowledge CollectionsBig Knowledge CollectionsBig Knowledge CollectionsBig Knowledge Collections
Rob Matthews: printed small sample of Wikipedia
Actually, a printedWikipedia corresponds to2000 Britannica volumes
Source:http://www.labnol.org/internet/wikipedia-printed-book/9136/
Actually, a printedWikipedia corresponds to2000 Britannica volumes
Source:http://www.labnol.org/internet/wikipedia-printed-book/9136/
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Background: 2006/7Background: 2006/7
Image: weinbrenner.single.arabzadeh. Architektenwerkgemeinschafthttp://www.wsa-nt.de
Computer Science branchof Max Planck Society, whichhas had 33 Nobel Prize winners
Computer Science branchof Max Planck Society, whichhas had 33 Nobel Prize winners
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Big Knowledge CollectionsBig Knowledge Collections
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YAGO2. Hoffart et al. WWW 2011.
YAGO2. Hoffart et al. WWW 2011.
Gerard de Melo
YAGO 1: 2007 YAGO 1: 2007
Big Knowledge GraphsBig Knowledge Graphs
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Big Knowledge GraphsBig Knowledge Graphs
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Gerard de Melo
Google Knowledge GraphGoogle Knowledge GraphGoogle Knowledge GraphGoogle Knowledge Graph
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Gerard de Melo
Mainstream AdoptionMainstream AdoptionMainstream AdoptionMainstream Adoption
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Mainstream AdoptionMainstream AdoptionMainstream AdoptionMainstream Adoption
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Mainstream AdoptionMainstream AdoptionMainstream AdoptionMainstream Adoption
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Mainstream AdoptionMainstream AdoptionMainstream AdoptionMainstream Adoption
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Solved:Solved:The Low-Hanging FruitThe Low-Hanging Fruit
Solved:Solved:The Low-Hanging FruitThe Low-Hanging Fruit
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What we should be able to askWhat we should be able to askWhat we should be able to askWhat we should be able to ask
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Europe: Could be UK, France, Germany, etc.!Europe: Could be UK, France, Germany, etc.!
What we should be able to askWhat we should be able to askWhat we should be able to askWhat we should be able to ask
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Spring: March, April, etc.Spring: March, April, etc.
What we should be able to askWhat we should be able to askWhat we should be able to askWhat we should be able to ask
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What we should be able to askWhat we should be able to askWhat we should be able to askWhat we should be able to ask
Are any talks at KG 2015about social media?
The following talks at KG 2015seem related to social media:
...Image: Microsoft Cortana
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What we should be able to askWhat we should be able to askWhat we should be able to askWhat we should be able to ask
Which talks at KG 2015 are mostrelated to my current projects?
The following talks at KG 2015seem most related to your work:
...Image: Microsoft Cortana
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Beyond Collections of FactsBeyond Collections of Facts
1. Knowledge Integration and Organization
2. Natural Language Coupling
3. Multimodal and Cognitive Knowledge
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Beyond Collections of FactsBeyond Collections of Facts
1. Knowledge Integration and Organization
2. Natural Language Coupling
3. Multimodal and Cognitive Knowledge
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The Web of DataThe Web of Data
LinkedData
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The Web of DataThe Web of Data
LinkedIntegrated
Data?
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Linked Data Is Still NotIntegrated Data
Linked Data Is Still NotIntegrated Data
Technical Challenges
● Different access mechanisms:Individual HTTP requests, SPARQL endpoints,Downloads
● Downloads often not available
● Servers offline
Technical Challenges
● Different access mechanisms:Individual HTTP requests, SPARQL endpoints,Downloads
● Downloads often not available
● Servers offline
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Linked Data Is Still NotIntegrated Data
Linked Data Is Still NotIntegrated Data
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Linked Data Is Still NotIntegrated Data
Linked Data Is Still NotIntegrated Data
https://commons.wikimedia.org/wiki/File:Gov%27t_Shutdown,_Locked.._-_Flickr_-_daveynin.jpg
A lot of dataA lot of datais locked awayis locked awayto some extentto some extent
A lot of dataA lot of datais locked awayis locked awayto some extentto some extent
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Linked Data Is Still NotIntegrated Data
Linked Data Is Still NotIntegrated Data
Semantic Challenges
● Different ontologies/schemas/vocabularies
● Links are incomplete
● Links are bad
● Data is sometimes bad or outdated
Semantic Challenges
● Different ontologies/schemas/vocabularies
● Links are incomplete
● Links are bad
● Data is sometimes bad or outdated
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Disconnected InformationDisconnected Information
IsA ΦPhilosopher
RudolfCarnap
Studied at Universityof
BerlinCarnap
Was Professor at
Einstein
bornInAlbert Einstein HU Berlin
Ulm
...
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Current SituationCurrent SituationCurrent SituationCurrent Situation
Alexandre Duret-Lutz https://www.flickr.com/photos/gadl/110845690/
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Goal: Integrated andOrganized KnowledgeGoal: Integrated and
Organized Knowledge
Theological Hall, Strahov Monastery Library, Prague
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EntityIntegration
EntityIntegration
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Entity IntegrationEntity Integration
Source: Bordes & Gabrilovich. KDD 2014 Tutorial
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Source: Peter Mika
Entity Integration:Challenges
Entity Integration:Challenges
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Entity-Level Integration
Gerard de Melo
Language-specific,Language-specific,Domain-specific,Domain-specific,Arbitrary DatabasesArbitrary Databases
Language-specific,Language-specific,Domain-specific,Domain-specific,Arbitrary DatabasesArbitrary Databases
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Entity Integration:Computing Similarity Scores
Entity Integration:Computing Similarity Scores
Are these two the same? Are these two the same?
Compare attributes, relations, textCompare attributes, relations, text→ Similarity score (weighted link)→ Similarity score (weighted link)
Are these two the same? Are these two the same?
Compare attributes, relations, textCompare attributes, relations, text→ Similarity score (weighted link)→ Similarity score (weighted link)
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ACL 2010AAAI 2013ACL 2010AAAI 2013
UseIdentity Linksto connectWhat isequivalent
Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
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Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
ACL 2010AAAI 2013ACL 2010AAAI 2013
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Merging Structured DataMerging Structured Data
Trentino Trentino-Alto Adige
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Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
One bad link is One bad link is enough to make aenough to make aconnected component connected component inconsistentinconsistent
One bad link is One bad link is enough to make aenough to make aconnected component connected component inconsistentinconsistent
ACL 2010AAAI 2013ACL 2010AAAI 2013
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OptimizationOptimizationProblem:Problem:
Make clustersMake clustersconsistentconsistent
OptimizationOptimizationProblem:Problem:
Make clustersMake clustersconsistentconsistent
Either by deletingEither by deletingedges or mergingedges or merging
conceptsconcepts
Either by deletingEither by deletingedges or mergingedges or merging
conceptsconcepts
Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
ACL 2010AAAI 2013ACL 2010AAAI 2013
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Min. cost solution:Min. cost solution:NP-hardNP-hard
APX-hardAPX-hard
Min. cost solution:Min. cost solution:NP-hardNP-hard
APX-hardAPX-hard
Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
ACL 2010AAAI 2013ACL 2010AAAI 2013
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Finally, use region growingFinally, use region growingalgorithm in the spiritalgorithm in the spirit
of Leighton & Rao 1988of Leighton & Rao 1988
Finally, use region growingFinally, use region growingalgorithm in the spiritalgorithm in the spirit
of Leighton & Rao 1988of Leighton & Rao 1988
Linear Program RelaxationLinear Program RelaxationLinear Program RelaxationLinear Program Relaxation
Approximation Guarantee:Approximation Guarantee:4ln(nq+1) 4ln(nq+1)
for n distinctness assertions,for n distinctness assertions,q=max |Dq=max |D
i,ji,j||
but independent of |Dbut independent of |Dii| !| !
Approximation Guarantee:Approximation Guarantee:4ln(nq+1) 4ln(nq+1)
for n distinctness assertions,for n distinctness assertions,q=max |Dq=max |D
i,ji,j||
but independent of |Dbut independent of |Dii| !| !
Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
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Linear Program RelaxationLinear Program RelaxationLinear Program RelaxationLinear Program Relaxation
Nice:Nice:
This generalizes theThis generalizes theHungarian AlgorithmHungarian Algorithm
from bipartite matchingsfrom bipartite matchingsto many non-standard kindsto many non-standard kinds
of matchingsof matchings
(cf. de Melo. AAAI 2013)(cf. de Melo. AAAI 2013)
Nice:Nice:
This generalizes theThis generalizes theHungarian AlgorithmHungarian Algorithm
from bipartite matchingsfrom bipartite matchingsto many non-standard kindsto many non-standard kinds
of matchingsof matchings
(cf. de Melo. AAAI 2013)(cf. de Melo. AAAI 2013)
Merging Structured DataMerging Structured DataMerging Structured DataMerging Structured Data
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TaxonomicIntegrationTaxonomicIntegration
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Question Answering
IBM's Jeopardy!-winning Watson system
Gerard de Melo
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Question Answering
IBM's Jeopardy!-winning Watson system
Gerard de Melo
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Question Answering
IBM's Jeopardy!-winning Watson system
Gerard de Melo
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De Melo & Weikum (2010).CIKM Best Interdisciplinary Paper AwardDe Melo & Weikum (2010).CIKM Best Interdisciplinary Paper Award
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
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De Melo & Weikum (2010).CIKM Best Interdisciplinary Paper AwardDe Melo & Weikum (2010).CIKM Best Interdisciplinary Paper Award
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
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De Melo & Weikum (2010).CIKM Best Interdisciplinary Paper AwardDe Melo & Weikum (2010).CIKM Best Interdisciplinary Paper Award
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
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De Melo & Weikum (2010).CIKM Best Interdisciplinary Paper AwardDe Melo & Weikum (2010).CIKM Best Interdisciplinary Paper Award
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
Taxonomic Integration:Taxonomic Integration:MENTA ApproachMENTA Approach
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Image: https://de.wikipedia.org/wiki/Datei:Bersntol_palae.jpg
Fersental(Bersntol, Valle dei Mòcheni)Fersental(Bersntol, Valle dei Mòcheni)
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
https://de.wikipedia.org/wiki/Datei:Language_distribution_Trentino_2011.png
Fersental(Bersntol, Valle dei Mòcheni)Fersental(Bersntol, Valle dei Mòcheni)
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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Use Identity ConstraintAlgorithm to form equivalence classes
Use Identity ConstraintAlgorithm to form equivalence classes
Markov Chain RandomWalk with Restartsto Rank Parents
Markov Chain RandomWalk with Restartsto Rank Parents
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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Taxonomic Integration:Taxonomic Integration:MENTAMENTA
Taxonomic Integration:Taxonomic Integration:MENTAMENTA
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KnowledgeIntegrationKnowledgeIntegration
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Knowledge IntegrationKnowledge Integration
X isAuthorOf YY writtenBy XX wrote YY writtenInYear Z
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UWN/MENTA
multilingual extension of WordNet forword senses and taxonomical information over 200 languages
Gerard de Melo
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Search Interfaces
“Which companies were created during the last century in Silicon Valley ?”
YAGO2:WWW 2011
Best Demo Award
YAGO2:WWW 2011
Best Demo Award
Gerard de Melo
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Example:Example:Lexvo.orgLexvo.orgExample:Example:Lexvo.orgLexvo.org
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Semantic WebSemantic WebJournal 2014Journal 2014Semantic WebSemantic WebJournal 2014Journal 2014
InterdisciplinaryInterdisciplinaryWork, e.g. inWork, e.g. inDigital HumanitiesDigital Humanities
InterdisciplinaryInterdisciplinaryWork, e.g. inWork, e.g. inDigital HumanitiesDigital Humanities
Lexvo.orgLexvo.org
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YAGO-SUMO: KnowledgeIntegration for ReasoningYAGO-SUMO: KnowledgeIntegration for Reasoning
SUMO
70000 axiomsopen source
SUMO
70000 axiomsopen source
YAGO-SUMO
SUMO's axiomsattached to
real-world instances,
overall many millions of axioms
YAGO-SUMO
SUMO's axiomsattached to
real-world instances,
overall many millions of axioms
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Smarter Question AnsweringSmarter Question Answering
Sutcliffe et al.Sutcliffe et al.Sutcliffe et al.Sutcliffe et al.
Gerard de Melo
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Beyond Collections of FactsBeyond Collections of Facts
1. Knowledge Integration and Organization
2. Natural Language Coupling
3. Multimodal and Cognitive Knowledge
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What is the capital of New Jersey?
??
??
Question AnsweringQuestion Answering
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Buy Jaguar in Beijing
Question AnsweringQuestion Answering
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Which meaning?Which meaning?
JaguarJaguar
Buy Jaguar in Beijing
Question AnsweringQuestion Answering
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“Do Macs now supportWindows?”
“Do Macs now supportWindows?”
Question AnsweringQuestion Answering
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Lexical Knowledge Bases
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Multilingual Lexical Knowledge
UWN (de Melo & Weikum 2009)
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MENTA
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UWN: Universal Wordnet
Gerard de Melo
Exploit millionsof translationsfrom the Web
Exploit millionsof translationsfrom the Web
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UWN: “Universal Wordnet”
Learn regularized regression model
to predict edge weightsfor a given pair of nodes
Learn regularized regression model
to predict edge weightsfor a given pair of nodes
Graph-based features, weighted by various edge weight metrics
Graph-based features, weighted by various edge weight metrics
Gerard de Melo
CIKM 2009CIKM 2009
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UWN: Universal Wordnet
over 1,000,000 words in over 100 languages
CIKM 2009CIKM 2009 ICGL 2008ICGL 2008Best Paper AwardBest Paper AwardICGL 2008ICGL 2008Best Paper AwardBest Paper Award
Gerard de Melo
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UWN: Getting StartedUWN: Getting Started
Simple API for JVM Languages
val uwn = new UWN(new File("plugins/"))for (m <- uwn.getMeanings("souris", "fra"))
println(m)
Or Just Download the TSV File
Simple API for JVM Languages
val uwn = new UWN(new File("plugins/"))for (m <- uwn.getMeanings("souris", "fra"))
println(m)
Or Just Download the TSV File
http://lexvo.org/uwn/
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UWNUWNUWNUWN
http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/
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UWNUWNUWNUWN
http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/
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UWNUWNUWNUWN
http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/
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UWNUWNUWNUWN
http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/ http://www.lexvo.org/uwn/
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LREC 2014LREC 2014
Etymological WordnetEtymological Wordnet
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Real Understanding?Real Understanding?
Knowledge Bases keep growing, butmuch of the Web is still not truly understood
Knowledge Bases keep growing, butmuch of the Web is still not truly understood
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Equivalent:
MetaWeb was acquired by Google.MetaWeb was just recently acquired by Google.MetaWeb, surprisingly, was acquired by Google.
Relation UnderstandingRelation Understanding
MetaWeb was bought out by Google.Google bought MetaWeb.Google acquired MetaWeb.MetaWeb was sold to Google.Google's acquisition of MetaWeb.Google's MetaWeb acquisition.and so on...
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Underlying frame: Commercial transfer
Capture the “who-did-what-to-whom”
Microsoft bought the patent from Nokia.Nokia sold the patent to Microsoft.The patent was acquired by Microsoft [from Nokia].The patent was sold [by Nokia] to Microsoft.
Relation Understanding:N-Ary Relations
Relation Understanding:N-Ary Relations
Buyer: Microsoft
Seller: Nokia
Product: The patent
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Relation IntegrationRelation Integration
YAGO: isMarriedTo predicateYAGO: isMarriedTo predicate
Freebase: Marriage EntityFreebase: Marriage Entity
Challenge:Modelling
Differences
Challenge:Modelling
Differences
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Relation Integration:FrameBase.org
Bringing knowledge into a standard formbased on natural language (FrameNet)
Bringing knowledge into a standard formbased on natural language (FrameNet)
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Background:Berkeley FrameNet Project
Background:Berkeley FrameNet Project
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Beyond Collections of FactsBeyond Collections of Facts
1. Knowledge Integration and Organization
2. Natural Language Coupling
3. Multimodal and Cognitive Knowledge
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MENTA: MediaMENTA: Media
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Knowlywood: Human ActivitiesKnowlywood: Human Activities
CIKM 2015CIKM 2015
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Knowlywood: Human ActivitiesKnowlywood: Human Activities
CIKM 2015CIKM 2015
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Learning Common-SenseLearning Common-SenseLearning Common-SenseLearning Common-Sense
WebChild
AAAI 2014WSDM 2014AAAI 2011
WebChild
AAAI 2014WSDM 2014AAAI 2011
Online: http://bit.ly/webchild
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Learning Common-SenseLearning Common-SenseLearning Common-SenseLearning Common-Sense
WebChild
(Tandon et al.WSDM 2014):
WebChild
(Tandon et al.WSDM 2014):
Online: http://bit.ly/webchild
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Comparative KnowledgeComparative KnowledgeComparative KnowledgeComparative Knowledge
AAAI 2014AAAI 2014
Joint Disambiguation:<bullet train, quicker than, bus>
<train, slower than, plane><plane, faster than, train><bus, slower than, plane>
Joint Disambiguation:<bullet train, quicker than, bus>
<train, slower than, plane><plane, faster than, train><bus, slower than, plane>
Online: http://bit.ly/webchild
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ImpLex:Implicative Commitments
Netflix admitted that they had developed
a prototype.
VSoft pretended that they had developed
a prototype.
Did theydevelop a
prototype?
Gerard de Melo
de Melo & de Paiva (2014)based onLauri Kartunnen's work
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ImpLex:Implicative Commitments
It is confusing thatApple has acquired
the technology.
It is unlikely thatApple has acquired
the technology.
Have theyacquired it?
?
de Melo &de Paiva 2014
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Word VectorsWord VectorsWord VectorsWord Vectors
x x
x xpetronia
sparrow
parched
aridxdry
x bird
http://www.wikihow.com/Read-a-Book-to-a-Baby-or-Infant#/Image:Read-a-Book-to-a-Baby-or-Infant-Step-5.jpg
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Why are Why are Word Vectors Successful?Word Vectors Successful?
Why are Why are Word Vectors Successful?Word Vectors Successful?
Matej Kren: Idiom. Prague Municipal Libraryhttps://www.flickr.com/photos/ill-padrino/6437837857/
● Fast Training on Large Amounts of Text● Joint Optimization (Mutual Influence
between Word Vectors)
● Fast Training on Large Amounts of Text● Joint Optimization (Mutual Influence
between Word Vectors)
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
syntactic
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
syntactic semantic!
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
semantic!syntactic syntactic?
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
semantic!syntactic syntactic? ?
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
semantic!syntactic ?
Word2Vec Solution:Subsampling
Word2Vec Solution:Subsampling
syntactic?
Jiaqiang Chen and Gerard de Melo 2015
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What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?What Goes into Word Vectors?
The Roman Empire was remarkablymulticultural, with ”a rather astonishingcohesive capacity” to create a senseof shared identity while encompassingdiverse peoples within its political system over a long span of time.
semantic!syntactic ?syntactic?
Jiaqiang Chen and Gerard de Melo 2015
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Textual Big DataTextual Big DataTextual Big DataTextual Big Data
Image: https://500px.com/photo/96738645/street-bookstore-by-pablo-tarrero
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Better Word Vectorsby Exploiting Knowledge
Better Word Vectorsby Exploiting Knowledge
BetterWord Embeddings
Joint Training
Jiaqiang Chen and Gerard de Melo 2015
Best Paper Awardat NAACL 2015Vector Space
Modeling Workshop
Best Paper Awardat NAACL 2015Vector Space
Modeling Workshop
Textual Big Data Knowledge
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Multilingual Word Vectors
Combine word2vec/GloVe Vectorswith multilingual lexical data from Wiktionary
Gerard de Melo
Available soon athttp://gerard.demelo.org/representations/
Contact me!
Available soon athttp://gerard.demelo.org/representations/
Contact me!
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Multilingual Word Vectors
Combine word2vec/GloVe Vectorswith multilingual lexical data from Wiktionary
Gerard de Melo
Available soon athttp://gerard.demelo.org/representations/
Contact me!
Available soon athttp://gerard.demelo.org/representations/
Contact me!
![Page 119: The Future of Knowledge Graphs Moving beyond Simple ...qngw2014.bj.bcebos.com/upload/kg3/KG 2015 - The... · Linked Data Is Still Not ... Bordes & Gabrilovich. KDD 2014 Tutorial](https://reader034.vdocument.in/reader034/viewer/2022051803/5b05a4da7f8b9a5c308bca3e/html5/thumbnails/119.jpg)
Multilingual Analogical ReasoningMultilingual Analogical ReasoningMultilingual Analogical ReasoningMultilingual Analogical Reasoning
Coffee is to Starbucksas ... is to Lipton?
System: Tea
Source vectors: GLOVE
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What we should be able to getWhat we should be able to getin the futurein the future
What we should be able to getWhat we should be able to getin the futurein the future
What is thisall about?
This is about the US budget debt ceiling,which needs to be extended. RepublicanSenate majority leader Mitch McConell
would like to...
Image: Microsoft Cortana
Huffington Post
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Summary
Knowledge Integration and Organization► MENTA taxonomy► Lexvo.org Linked Data
Natural Language Coupling► Universal WordNet► FrameBase.org
Multimodal and Cognitive Knowledge► Knowlywood► WebChild
More Information:
More Information:
Gerard de Melo