ontology-based information extraction systems (obies 2008)

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asstandardsearchenginequeriesusingoff-the-shelfsearchengineAPIs.ThiscircumventstheneedtolinearlyprocessthewholeWeb(seee.g.[3]).Someapproachesperformpatterninductioninaniterativefashioninacyclicapproachwhichusesthenew examplesderivedinoneiterationfortheinductionofnewpatternsinthenextiteration [4,1].Inthispaperwefollowthislatterapproachandinparticularexaminemorein detailtheempiricalcomplexityofthepatterninductionstep.Asintheseapproaches theinductionofpatternsproceedsinabootstrapping-likefashion,thecomplexityofthe patterninductionstepcruciallydeterminesthetimecomplexityofthewholeapproach. Earlierimplementationsoftheapproachhaveusedgreedystrategiesforthepairwise comparisonoftheoccurrencesofseedexamples.InthispaperweshowhowtheApriorialgorithmfordiscoveringfrequentitemsetscanbeusedtoderivepatternswitha minimalsupportinlineartime.Ourempiricalevaluationshowsthatwiththisapproach patterninductioncanbereducedtolineartimewhilemaintainingextractionquality comparable(andevenmarginallybetter)toearlierimplementationsofthealgorithm. Theremainderofthispaperisorganizedasfollows.Inthenextsectionwedescribetheapproachofpattern-basedrelationextractionusingWebsearchenginesin moredetail.InsectionPatternInductionasFrequentItemsetMining,wegiveabrief introductiontoFrequentItemsetMiningbeforedescribinghowitisappliedinorder toinducepatternsforrelationextraction.WedescribeourexperimentalresultsinsectionExperimentalResults,beforediscussingrelatedworkandgivingsomeconcluding remarks. 2 Iterative Pattern Induction Thegoalofpatterninductionis,givenasetofseedexamples(pairs) Sofarelation R aswellasoccurrences Occ(S)inthecorpus(theWebinourcase)oftheseseeds,to induceasetofpatterns Pwhicharegeneralenoughtoextractmanymoretuplesstandingintherelation R(thushavingagoodcoverage)andwhichatthesametimedonot overgenerateinthesensethattheyproducetoomanyspuriousexamples.Thechallengingissueshereareontheonehandthatthehypothesisspaceishuge,correspondingto thepowersetofthesetofpossiblepatterns P representingabstractionsoverthesetof occurrences Occ(S).Wewilldenotethishypothesisspaceas 2 P .Ontheotherhand, thecompleteextension extRoftherelation Risunknown(itisthegoalofthewhole approachtoapproximatethisextensionascloselyaspossibleattheendofthecycle), suchthatwecannotuseittocomputeanobjectivefunction: o : 2 P → Rtodetermine thepatterns’accuracywithrespecttotheextension extR. ThegeneralalgorithmforiterativeinductionofpatternsispresentedinFigure1. Itsubsumesmanyoftheapproachesmentionedintheintroductionwhichimplement similarbootstrapping-likeprocedures.Thekeyideaistoco-evolve P (whichatthe beginningisassumedtobeempty)aswellasaconstantlygrowingsetofexamples S whichatthebeginningcorrespondstotheseedexamples.Thecandidatepatternscanbe generatedinagreedyfashionbyabstractingovertheoccurrences Occ(S).Abstracting requiresfindingcommonproperties,whichinprincipleisaquadratictaskasitrequires pairwisecomparisonbetweenthedifferentoccurrences.
ITERATIVE PATTERNINDUCTION(PatternsP ′ , TuplesS ′ ) 1 S ← S ′ 2 P ← P ′ 3 while not DONE 4 do Occt ←MATCH-TUPLES(S) 5 P ← P ∪LEARN-PATTERNS(Occt) 6 EVALUATE-PATTERNS(P) 7 P ← {p ∈ P |PATTERN-FILTER-CONDITION(p)} 8 Occp ←MATCH-PATTERNS(P) 9 S ← S +EXTRACT-TUPLES(Occp) 10 EVALUATE-TUPLES(S) 11 S ← {t ∈ S |TUPLE-FILTER-CONDITION(t)} Fig. 1.Iterativepatterninductionalgorithmstartingwithinitialtuples S ′ or(alternatively)patterns P ′ . Thealgorithmstarts with a set of initial tuples S ′ of the relation in question– socalledseeds–andloopsoveraprocedurewhichstartsbyacquiringoccurrences ofthetuplescurrentlyin S (e.g.byqueryingasearchenginewith "Stockholm" "Sweden")fortherelation locatedIn.Furtherpatternsarethenlearnedbyabstractingoverthetextoccurrencesofthetuples.Thenewpatternsarethenevaluatedand filteredbeforetheyarematched.Aresultingpatterncouldbe“flightsto ARG1, ARG2 from ∗airport”andthusmaycontainwildcardsandargumentplaceholders.Fromthese matches,newtuplesareextracted,evaluatedandfiltered.TheprocessisrepeateduntilaterminationconditionDONEisfulfilled.Thelearningisthusinductiveinnature, abstractingoverindividualpositiveexamplesinabottom-upmanner. Forourexperimentswehaveusedtheimplementationoftheabovealgorithmas describedin[3].Theyhaveshowninpreviousworkthatinabsenceofanobjective functiontomaximize,wecanreasonablyestimatethequalityoftheset P ofpatterns byaheuristicfunction.Amongthedifferentfunctionsexaminedin theabovementionedwork,asimplefunctionwhichassessesthequalityofapatternonthebasisof itssupport,i.e.thedifferentoccurrenceswhichitwasgeneratedfromandtherefore covers,isshowntobeagoodchoicecomparedtoothermoreelaboratemeasuressuch asthepointwisemutualinformationusedintheEspresso[12]andothersystems(e.g. KnowItAll[9]).Therefore,areasonablechoiceistoselectthosepatternswhichhavea minimalsupportandmeetsomeheuristicsyntacticcriteriatopreventtoogeneralpatterns 1 .Wedescribeinthefollowingsectionhowthisproblemcanbeformulatedasthe oneofdeterminingfrequentitemsetsusingthewell-knownapriorialgorithm.Withthis move,wealsoreducethecomplexityofthepatterninductionstepfromquadraticto linearinthenumberofoccurrences. 3 Pattern Induction as Frequent Itemset Mining Inourapproach,wetranslatetextualoccurrencesofacertainrelationintosetrepresentationsandusetheApriorialgorithmtofindpatternsintheseoccurrencesthatexceeda certainminimumsupport.Thistaskistypicallycalledfrequentitemsetmining(FIM). 1 Inparticular,weensurethatthepatternshaveaminimalnumberoftokenconstraints(andnot onlywildcards)aswellasthattheyhavebeengeneratedfromatleasttwodifferenttuples.
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