From Big Data to Valuable Knowledge

From Big Data to Valuable Knowledge

Gerard de Melo, Tsinghua Universityhttp://gerard.demelo.org

From Big Data to Valuable Knowledge

Gerard de Melo, Tsinghua Universityhttp://gerard.demelo.org

25 Years of the World Wide Web:1989−2014


http://geekcom.wordpress.com/2009/03/19/

Tim Berners-Lee

Big Data on the WebBig Data on the WebBig Data on the WebBig Data on the Web

Theological Hall, Strahov Monastery Library, Prague

Main Challenge So Far: ScaleMain Challenge So Far: ScaleMain Challenge So Far: ScaleMain Challenge So Far: Scale

Matej Kren: Idiom. Prague Municipal Libraryhttps://www.flickr.com/photos/ill-padrino/6437837857/

Developing for ScalabilityDeveloping for Scalability

officialHadoopWordCount v1.0

excludingimportsandimprovementsin WordCountv2.0


import com.twitter.scalding._

class WordCountJob(args : Args) extends Job(args) { TextLine(args("input")) .flatMap('line -> 'word) { line : String => line.split("""\s+""") } .groupBy('word) { _.size } .write(Tsv(args("output")))}


Apache Spark Twitter's Scalding

Knowledge OrganizationKnowledge Organization

Image: http://commons.wikimedia.org/wiki/File:Mundaneum_Tir%C3%A4ng_Karteikaarten.jpg

Universal Bibliographic Repertory(Repertoire Bibliographique Universel, RBU)by Paul Otlet and Henri La Fontaine in 1895

index cards with answers to queries



Knowledge OrganizationKnowledge Organization

Image: Mundaneum





Alex Wright: This was a sort of“analog search engine”

Alex Wright: This was a sort of“analog search engine”

Current Challenge:Current Challenge:Knowledge OrganizationKnowledge Organization

Current Challenge:Current Challenge:Knowledge OrganizationKnowledge Organization

Alexandre Duret-Lutz https://www.flickr.com/photos/gadl/110845690/



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



Source: Ivan Herman. Introduction to Semantic Web Technologies

Data reallyneeds to be more connected!

Data reallyneeds to be more connected!

The Web of Data:Linked Data

The Web of Data:Linked Data

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

The Web of Data:Lexvo.org

The Web of Data:Lexvo.org

Source: Peter Mika

Entity Integration:Challenges


ACL 2010AAAI 2013ACL 2010AAAI 2013



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




Min. cost solution:Min. cost solution:NP-hardNP-hard

APX-hardAPX-hard

Min. cost solution:Min. cost solution:NP-hardNP-hard

APX-hardAPX-hard

Entity IntegrationEntity Integration


Our Solution:Our Solution:Use Linear Program andUse Linear Program andthen apply region growingthen apply region growing

techniquestechniques

→ → LogarithmicLogarithmicApproximationApproximation

GuaranteeGuarantee

Our Solution:Our Solution:Use Linear Program andUse Linear Program and

then apply region growingthen apply region growingtechniquestechniques

→ → LogarithmicLogarithmicApproximationApproximation

GuaranteeGuarantee

Taxonomic Links

a user wantsa list of

„Art Schools in Europe“

Taxonomic Integration:MENTA Approach

De Melo & Weikum (2010).CIKM Best Interdisciplinary Paper AwardDe Melo & Weikum (2010).CIKM Best Interdisciplinary Paper Award

UWN/MENTA: multilingual extension of WordNet forword senses and taxonomical information over 200 languages

Gerard de Melo

UWN/MENTAUWN/MENTAUWN/MENTAUWN/MENTA

Relation ExtractionRelation Extraction

Images: Denilson Barbosa, Haixun Wang, Cong Yu. Shallow Information Extraction for the Knowlege Web

Scaling Up:Tandon, de Melo & Weikum.AAAI 2011, COLING 2012

Scaling Up:Tandon, de Melo & Weikum.AAAI 2011, COLING 2012

Equivalent:

MetaWeb was acquired by Google.MetaWeb was just recently acquired by Google.MetaWeb, surprisingly, was acquired by Google.

Relation IntegrationRelation Integration

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...

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.


Buyer: Microsoft

Seller: Nokia

Product: The patent

Relation Integration:FrameBase.org

Bringing knowledge into a standard formbased on natural language (FrameNet)

Bringing knowledge into a standard formbased on natural language (FrameNet)


X isAuthorOf YY writtenBy XX wrote YY writtenInYear Z


YAGO: isMarriedTo predicateYAGO: isMarriedTo predicate

Freebase: Marriage EntityFreebase: Marriage Entity

Challenge:Modelling

Differences

Challenge:Modelling

Differences

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

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

Real Understanding?

Source: CMU NELL Browser 2015-03-17

Over 4000countries

with >90%confidence

Over 4000countries

with >90%confidence

NoisyPatterns

NoisyPatterns

Future Challenge: Future Challenge: Real UnderstandingReal UnderstandingFuture Challenge: Future Challenge:

Real UnderstandingReal Understanding

Voynich Manuscript, early 15th century

From Big Data to KnowledgeFrom Big Data to KnowledgeFrom Big Data to KnowledgeFrom Big Data to Knowledge

Image:Brett Ryder

Machine LearningMachine Learning

Examples ProbablyIncorrect!

LearningLearning PredictionPrediction

ClassifierModel

Incorrect

Correct

Better Machine LearningBetter Machine Learning

Examples ProbablyIncorrect!

LearningLearning PredictionPrediction

Incorrect

CorrectClassifierModelBetter

Model!

+BetterLabelsfor Test

Data

ConversationConversation

Always there toanswer questionsAlways there toanswer questions

Learning Common-SenseLearning Common-Sense

Gerard de Melo

I'm cold.

Warm coffee and tea are available atCosta Coffee just around the corner.But don't forget your meeting with

Linda in half an hour!

Learning Common-Sense:From Big Data?

Learning Common-Sense:From Big Data?

WebChild

AAAI 2014WSDM 2014AAAI 2011

WebChild

AAAI 2014WSDM 2014AAAI 2011

WebChild: LearningCommon-Sense From Big Data

WebChild: LearningCommon-Sense From Big Data

Why do you think Mary put on thering at the end of the movie?

Yes, that was powerful scene. The factthat she put it on after reading theletter from her mother indicates

that she may have changedher mind about the value of ...

Future: Learning Advanced Common-Sense Knowledge?Future: Learning Advanced

Common-Sense Knowledge?

SummarySummarySummarySummary

Big Data is radically changing the world

Main Challenge in the Past: Scale

Main Current Challenge: Organization1. Entity Integration2. Taxonomic Integration3. Relation Extraction and Integration

Main Future Challenge: Real Understandingby learning from weak signals

From Big Data to Valuable Knowledge

Technology

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