ontology-based information extraction systems (obies 2008)

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Table of Contents Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Sebastian Blohm, Philipp Cimiano Ontology-based information extraction in agents hands . . . . . . . . . . . . . . . . 11 Brigitte Endres-Niggemeyer Information Extraction Based on Extraction Ontologies: Design, Deployment and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Martin Labsky, Vojtech Svatek, Marek Nekvasil Relation Validation via Textual Entailment . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Rui Wang, Guenter Neumann
Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining SebastianBlohmandPhilippCimiano InstituteAIFB,UniversitätKarlsruhe(TH) Abstract. Inthispaper,weaddresstheproblemofextractingrelationalinformationfromtheWebatalargescale.Inparticularwepresentabootstrapping approachtorelationextractionwhichstartswithafewseedtuplesofthetarget relationandinducespatternswhichcanbeusedtoextract furthertuples. Our contributioninthispaperliesintheformulationofthepatterninductiontaskasa well-knownmachinelearningproblem,i.e.theoneofdeterminingfrequentitemsetsonthebasisofasetoftransactionsrepresentingpatterns.Theformulationof theextractionproblemasthetaskofminingfrequentitemsetsisnotonlyelegant, butalsospeedsupthepatterninductionstepconsiderablywithrespecttopreviousimplementationsofthebootstrappingprocedure.Weevaluateourapproach intermsofstandardmeasureswithrespecttosevendatasetsofvaryingsizeand complexity.Inparticular,byanalyzingtheextractionrate(extractedtuplesper time)weshowthatourapproachreducesthepatterninductioncomplexityfrom quadratictolinear(inthesizeoftheoccurrencestobegeneralized),whilemantainingextractionqualityatsimilar(orevenmarginallybetter)levels. 1 Introduction Aproblemwhichhasreceivedmuchattentioninthelastyearsistheextractionof(binary)relationsfromtheWeb.Automaticextractionofrelationsisusefulwhenevertheamountoftexttoanalyzeisnotmanageablemanually.Asanexample,acarmanufacturermaywanttomonitorupcomingmarketdevelopmentsbyanalyzingnewsandblogs ontheWeb.RelationextractioncanextractthepresentedAtrelationinordertocompile alistofupcomingcarmodelsandwheretheywillbepresented(e.gpresentedAt(Audi Q7,DetroitMotorShow)).Toaddressthisproblem,severalsupervisedapproacheshave beenexaminedwhichinduceaclassifierfromtrainingdataandthenapplyittodiscover newexamplesoftherelationinquestion.Theseapproachestypicallyworkonaclosed corpusandrelyonpositiveand(implicit)negativeexamplesprovidedintheformof annotations[18,8]orahandfulofpositiveandnegativeexamples[5].Theobvious drawbackofsuch methodsisthattheycan inherentlynotscale totheWeb asthey wouldrequiretheapplicationoftheclassifiertothewholetextualdataontheWeb,thus beinglinearinitssize. AlternativeapproachestoaddresstheproblemofextractingrelationsfromtheWeb havebeenpresented(wediscussacoupleofsystemsbelow).Theseapproachesrelyon theinductionofpatternsonthebasisofoccurrencesofafewexamplesoftherelation inquestion.Suchexplicittextualpatternsallowtotakeashortcuttolinearlyscanning thewholeWebbyrelyingonstandardindexstructurestoevaluatethestringpatterns
Page 1 and 2: Proceedings 1st International and K
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