2httpjettycodehausorgjetty3httpwwwgraphvizorg4httpdojotoolkitorgeditortoinserteditanddeleterulesandobservetheirimmediatee ectonthesearchresultsInparticularwewillshowhowdesiredre ID: 99091
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Figure2:Screenshot:provenancetrackingourimplementation).Thesecondpropertyisinformal:theruleshouldbenaturalinthesensethatitcorrespondstoasemanticallyjustiedrewrite(sothatitmakessensetotheadministrator).Forexample,skills!expertiseisnaturalasthetwosideshavesimilarmeanings,andsoisdownload!issiinIBMasISSIisaprominentdownload/installationtoolinsideIBM.Toranktheextenttowhicharuleisnatural,weuseasupervisedmachine-leaningapproach.2.4ImplementationThetoolkitisbuiltasaWebapplication.OntheserversidetheimplementationisbyJava(Servlet)technologycar-riedbyJettyWebServer.2WeuseGraphviz3tocomputethelayoutoftheprovenancetrellisgraph.OntheclientsidethetoolkitusestheDojoToolkit4forthegenerallayout.DataiscommunicatedbetweentheclientandtheservermainlyinJSONformat.3.DEMONSTRATIONThegoalofthedemonstrationistoillustratehowtheprin-ciplesofprogrammablesearchcanberealizedandfacilitatedbythepropertoolingandvisualization.Ourplanisfortheaudiencetoexperienceusingthetoolkitasitisintendedtobeusedbysearchadministratorsintheenterprise(andasitiscurrentlybeingusedbytheminIBM).Specically,fol-lowingabriefdescriptionofthebackgroundandunderlyingsearchparadigmwewilldemonstratevarioususecases.Provenancevisualization.Wewillconsidervarioussearchqueriesandexplaintheprovenanceofthetopresultsforthesequeries(includingthecorrespondingruntimerules,re-formulations,eldindicesandrankingvectors).Desideratuminvestigation.Foroneormoresearchqueries,wewillconsiderdesiredsearchresultsthatarelowranked,andusetheprovenancevisualizationtoexplaintheirrelativelowrankingcomparedtootherresults.Runtimeprogramming.Wewillusetheembeddedrule 2http://jetty.codehaus.org/jetty/3http://www.graphviz.org/4http://dojotoolkit.org/editortoinsert,editanddeleterules,andobservetheirim-mediatee ectonthesearchresults.Inparticular,wewillshowhowdesiredresultsarepushedtothetopbyphrasingappropriaterewriterules.Rulesuggestion.Wewillconsidercasesoflow-rankeddesiredresultsandaskthetoolkittosuggestrewriterules.Wewillfurtherdiscussthequalityoftheserulesandexplaintheunderlyinglearningmachinery.AcknowledgmentsWearedeeplygratefultoNicoleDriandGayleS.Borgeforprovidingvaluablefeedbackonthetoolkit.WealsothankShivakumarVaithyanathanforinsightfuldiscussions.4.REFERENCES[1]G.Agarwal,G.Kabra,andK.C.-C.Chang.Towardsrichqueryinterpretation:walkingbackandforthforminingquerytemplates.InWWW,2010.[2]O.Alhabashneh,R.Iqbal,N.Shah,S.Amin,andA.E.James.Towardsthedevelopmentofanintegratedframeworkforenhancingenterprisesearchusinglatentsemanticindexing.InICCS,2011.[3]Z.Bao,B.Kimelfeld,andY.Li.Automaticsuggestionofquery-rewriterulesforenterprisesearch.InSIGIR,2012.[4]P.A.Dmitriev,N.Eiron,M.Fontoura,andE.J.Shekita.Usingannotationsinenterprisesearch.InWWW,2006.[5]R.Fagin,B.Kimelfeld,Y.Li,S.Raghavan,andS.Vaithyanathan.Rewriterulesforsearchdatabasesystems.InPODS,2011.[6]R.Fagin,R.Kumar,K.S.McCurley,J.Novak,D.Sivakumar,J.A.Tomlin,andD.P.Williamson.Searchingtheworkplaceweb.InWWW,2003.[7]D.Hawking.Challengesinenterprisesearch.InADC,volume27ofCRPIT,2004.[8]Y.Li,R.Krishnamurthy,S.Vaithyanathan,andH.V.Jagadish.Gettingworkdoneontheweb:supportingtransactionalqueries.InSIGIR,2006.[9]I.Szpektor,A.Gionis,andY.Maarek.Improvingrecommendationforlong-tailqueriesviatemplates.InWWW,2011.[10]S.Vaithyanathan.Buildingsearchsystemsfortheenterprise,2011.SIGIR'11industrialtrackkeynote.[11]H.Zhu,S.Raghavan,S.Vaithyanathan,andA.Loser.Navigatingtheintranetwithhighprecision.InWWW,2007.