Entity-RelationshipExtractionfromWikipediaUnstructuredText

RadityoEkoPrasojo (Rido)PhDStudent@KRDB,FreeUniversityofBozen-Bolzano

Supervisedby:Mouna Kacimi &WernerNutt

20.07.16,Bilbao,Spain

Automaticallygenerated Manuallycurated

Automatedextractionwithout(yet)aKBasaresult

KnowledgeVault[1]

KnowledgeGraph

NELL[2]

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Infobox completion [3][4]

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WherewasObamaborn?

WhoarethechildrenofObama?

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WhenwasObamaborn?

WhoarethechildrenofObama?

Yeswecan!

Honolulu, HawaiiMaliaandSashaObama

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WhichareObama’sfavourite sportsteam?

DoesObamahavepets?

OurgoalistoenrichexistingKnowledgeBasesbyextractingnewfactsintheformofmachine-readableentity-relationshipfromWikipediaunstructuredtext.

Specificfocus:RDF

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Whyisitdifficult?

• Theextractionproblem• Entityextraction&disambiguation• Relationextraction

• Therepresentationproblem• Lackofpredefinedschema/ontology• Topic-independency• Complexfactrepresentation

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Whyisitdifficult?Example

• “Obamaisasupporterofthe ChicagoWhiteSox”• Straightforward,singletoninformation• Puresyntacticextractionpossible• Barack_Obama supporterOf Chicago_White_Sox

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Whyisitdifficult?Example

• “Obamaisasupporterofthe ChicagoWhiteSox”• Straightforward,singletoninformation• Puresyntacticextractionpossible• Barack_Obama supporterOf Chicago_White_Sox

• “He isalsoprimarily a ChicagoBears footballfaninthe NFL,butinhischildhoodandadolescencewas a fanofthePittsburghSteelers”• Complex,multipleinformation• Semanticunderstandingnecessary• …howdowerepresentthis?

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Example:representingcomplexfact

• “He isalsoprimarily a ChicagoBears footballfaninthe NFL,butinhischildhoodandadolescencewas a fanofthePittsburghSteelers”• Barack_Obama footballFan Chicago_Bears in NFL• supporterOf vsfootballFan• IsitnecessarytoincludeNFL inthewholerelations?• Whatabouttheadjectiveprimarily?Whatinformationdoesitimply?

• Barack_Obama fanOf Pittsburgh_Steelers• fanOf vs supporterOf• Missingthetimeinformationreferredin“inhischildhoodandadolescencewas”

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Approach

• Documentpreprocessingtoannotateallentityoccurences.• Grammaticaldependencytoextract(candidate)relations.

• Separationbetweentheextractionproblemandtherepresentationproblem• Wefirstextractallcandidaterelationsandthenlaterapplysemanticrefinementforbetterrepresentation.

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Preliminaryresults

• Groundtruthmanuallycuratedfrom25Wikipediaarticlesoffamouspeople.• Preprocessing• 4handcraftedextractionrulesleveraginggrammaticaldependency

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Ongoingwork• Automatedrulemining• Semanticrefinementforknowledgerepresentation• Ontologybuilding

• Namingandtaxonomyofentities,classes,andrelations• Handlingcomplexfact

• Obamaappointsxasyinz• Handlingmodality,adjectives,andsentiment

• “Inthepast”,“itisrumoured that”,“itisnottruethat”

• Futureevaluation• Biggergroundtruth(amount+topiccoverage)• EvaluatehowwellweenrichexistingKBs

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Futurework

• Metadataextraction• Dataquality,datacompleteness

• NaturallanguagequestionansweringbasedontheenrichedKB.

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