taxonomy and search patterns v3 - green chameleon and search patterns … · in the general...

Taxonomy and Search Patterns

TaxonomyandSearchPatternsforEnhancedSearchandDiscoveryPatrickLambe

Taxonomy alone is limited in what it can do. Search alone is also limited. Together, they become much smarter. If taxonomy and search are integrated, they can be very powerful and vastly improve the user experience. This paper borrows from previous work on search experience and search patterns, and adds a layer of commentary and examples connecting these patterns to the interaction between taxonomy and search.

Table of Contents

PATTERN 1. SEARCH ZONES ............................................................................ 41.1 About This Pattern ...................................................................................... 41.2 Benefits ........................................................................................................... 41.3 Illustrations ................................................................................................... 41.4 Dependencies ............................................................................................... 71.5 Potential Application .................................................................................. 7

PATTERN 2. AUTOCOMPLETE/AUTOSUGGEST .............................................. 82.1 About the Pattern ........................................................................................ 82.2 Benefits ........................................................................................................... 82.3 Illustrations ................................................................................................... 92.4 Dependencies ............................................................................................. 112.5 Potential Application ................................................................................ 12

PATTERN 3. BEST BETS ................................................................................... 133.1 About This Pattern .................................................................................... 133.2 Benefits ......................................................................................................... 133.3 Illustrations ................................................................................................. 133.4 Dependencies ............................................................................................. 143.5 Potential Application ................................................................................ 15

PATTERN 4. FACETED NAVIGATION .............................................................. 164.1 About This Pattern .................................................................................... 164.2 Benefits ......................................................................................................... 164.3 Illustrations ................................................................................................. 164.4 Dependencies ............................................................................................. 194.5 Potential Application ................................................................................ 20

PATTERN 5. RICH SNIPPETS ........................................................................... 215.1 About This Pattern .................................................................................... 215.2 Benefits ......................................................................................................... 215.3 Illustrations ................................................................................................. 215.4 Dependencies ............................................................................................. 235.5 Potential Application ................................................................................ 24

PATTERN 6. FROM SEARCH TO ACTION ....................................................... 256.1 About This Pattern .................................................................................... 25

6.2 Benefits ......................................................................................................... 256.3 Illustrations ................................................................................................. 256.4 Dependencies ............................................................................................. 276.5 Potential Application ................................................................................ 28

PATTERN 7. SUGGESTIONS / SEARCH EXPANSION .................................... 297.1 About This Pattern .................................................................................... 297.2 Benefits ......................................................................................................... 297.3 Illustrations ................................................................................................. 297.4 Dependencies ............................................................................................. 317.5 Potential Application ................................................................................ 32

PATTERN 8. CONTEXT BUILDING ................................................................... 338.1 About This Pattern .................................................................................... 338.2 Benefits ......................................................................................................... 338.3 Illustrations ................................................................................................. 338.4 Dependencies ............................................................................................. 358.5 Potential Application ................................................................................ 35

PATTERN 9. HIGHLIGHTING ............................................................................. 379.1 About This Pattern .................................................................................... 379.2 Benefits ......................................................................................................... 379.3 Illustrations ................................................................................................. 379.4 Dependencies ............................................................................................. 389.5 Potential Application ................................................................................ 38

PATTERN 10. VISUALIZING RELATIONSHIPS AND INTENSITY ................... 3910.1 About This Pattern ................................................................................. 3910.2 Benefits ....................................................................................................... 3910.3 Illustrations ............................................................................................... 3910.4 Dependencies ........................................................................................... 4210.5 Potential Application .............................................................................. 42

REFERENCES .................................................................................................... 43

PATTERN 1. SEARCH ZONES

1.1 About This Pattern Zoningofsearchallowsthesearchadministratorstoscopethecontentareasthatwillbesearched,anddefinethesearchrules,dependingonthepagewherethesearchisinitiated.Forexample,onGoogle,asearchinitiatedfromSingaporewillreturnresultsthataremostrelevanttoSingaporeanaudiences,first.Withinanenterprise,asearchinitiatedfromwithinacompliancefunctionwillsearchandreturnresultsmostrelevanttothatfunction.

1.2 Benefits Searchzoningallowsthesearchexperiencetobecustomizeddependingontheknowninformationabouttheuser,usingtheirstartingpointtomakeinferencesabouttheircontextandneeds.Zoninghelpsuserstargettheirsearchonspecificcontentcollectionsthatarelikelytobemostrelevanttothem.Thiswillimprovesearchrecallandprecision.Recallisimprovedbecauseirrelevantitemsfromothersectionsarenotincluded,andprecisionisimprovedbecausethechanceofirrelevantitemsfromothersectionssurfacingtothetopoftheresultsetisreduced.Searchzonescanalsobeusedtocustomizeothersearchpatterns(describedintherestofthisreport)toparticularaudiencesinatargetedway.Forexample,atechnicalaudiencemaybepromptedwithauto-suggestedtechnicaltermsappropriatetotheirfunction.Rankingofresultscanbechangeddependingonwherethesearchisinitiated.

1.3 Illustrations AGooglesearchfor“twintowers”initiatedfromMalaysiawillshowthePetronasTwinTowersinKualaLumpuratthetopofthesearchresults.FromtheUSA,thesamesearchstartswithentriesfortheWorldTradeCenterinNewYorkandtheSeptember11attacks.FromTaiwan,thesearchresultshavecuesintraditionalMandarin,andanoptiontoswitchtoresultsinEnglish.

TheBBCNewswebsitehelpsitsuserstonavigateanextremelyrichanddynamicsitebyinvitingthemtofocustheirsearchesbyexplicitlyselectingthesearchzonestheywant.

Inapropertydevelopmentcompany,searchresultsfromwithintheStaffDirectorysectionoftheintranetareshowntheExpertisetaxonomyfacetandtheDepartmentmetadatafilterasthefirstoptionsshowntostafftohelpthemrefinetheirsearchresultsfurther.IntheGeneralResourcessectionoftheintranet,thefirstfilteringfacetsdisplayedareBusinessActivitiesandDocumentTypes.

1.4 Dependencies Effectivesearchzoninginvolvesagoodunderstandingofhowdifferentusercontextsanddifferentcontenttypesdrivedifferentsearchstrategies.Aswithmanyoftheothersearchpatternsdescribedinthisreport,itrequiresadetailedknowledgeofdifferentuserneeds,andaccuratemodelingofthemostimportantcontenttypes.

1.5 Potential Application Searchzoningcanbeusedtogivesemi-personalizedsearchexperiencestousers,basedonwhatisknownabouttheirrole,functionorinterests.Forexample,taxauditorsmighthavetaxonomycategoriesandresourcespagesrelatedtothatfunctionzonedforthem.Thisalsoinvolves(a)beingabletopredictusefulcontentaccordingtodifferentcontextswithsomeconfidence,and(b)beingabletodesignastartingpointforthetaskthatallowsspecificsearchzonestobeassociatedwiththesearchfunctionsupportingthattask.E.g.searchinterfacescouldbepushedoutintotaskorientedworkingenvironments,andconfiguredtomakecallsonthecentralresourcefromwithinstaff’sownworkspace.

PATTERN 2. AUTOCOMPLETE/AUTOSUGGEST

2.1 About the Pattern Inautocomplete,asmalldropdownboxappearsasausertypesasearchquery,andashortlistofitemsappearsunderthesearchinputbox,theseitemsaremodifiedinrealtimeastheusercontinuestyping.Theusercanstoptypingandscrolldowntoselectanyoftheitemsatanytime.Theitemscanbe:

• thesaurusortaxonomytermsthatareaclosematchtowhattheuserhastypedsofar–whenclicked,thesysteminitiatesasearchresultspagefortheassociatedtaxonomyterm

• directlinkstocontent(documentsordataprofiles)whosemetadata(e.g.title,description)containsthetermorphrasetypedsofar–whenclicked,thesystembringstheusertothedocumentsummarypage

• alistofpopularsearchesbyotherusers,orbythatuser,associatedwiththetermorphrasetypedsofar–whenclicked,thesystembringstheusertothatsearchresultspage.

ThisfunctionalitycanbecombinedwiththecontextualinformationinagivenSearchZone(Pattern1).

2.2 Benefits Thepurposeofautocompleteistogettheusertotheirintendedoutcomefasterbypredictingwhatmightbeusefultothem.Autocompletegivestheuserapreviewofpotentialsearchresultsevenbeforetheyhavecompletedtheirquery.Itallowstheusertoshort-circuitthesearchcyclebyallowingthemtogotospecificsearchresultsoreventothecontentitself,withouthavingtoreviewmoregenericsearchresultspages.Whenassociatedwithtaxonomyorthesaurusterms,autocompletealsoperformsasubtle“convergence”effect,educatingtheuserontheusageofsubjecttermsforwhichtherearegoodresults,andmitigatingerrorsfrompoorsearchqueries(ambiguous,toocomplexorwithmisspellings).

2.3 Illustrations

IntheGoogleexample,Ihavetypedthethreeletters“fed”andGoogleissuggestingtwoprevioussearchesIhaveconductedbeginningwith“fed”andtwoadditionalpopularsearches.Clickingonanyofthesewilllaunchthesearchresultspageforthetermswithmeneedingtocompletethequeryandclicksubmit.

IntheAmazonexample,autosuggestinthebooksdepartmentwillleadmetosearchresultspagesfortermsthatappearinbooktitles.

InSkype,autocompleteshowsusernamesthatcontainthetypedphrase,butbecausetheintendedoutcomeinSkypeisactuallyacallingormessagingfunction,italsodisplayspreviouscallgroupscontainingnameswiththetypedtextstring.

Autosuggestcanalsobeusedtodisambiguateclose,butdistinctconceptsforwhichthereisdifferentinformation.TheBBCweatherforecastusesautosuggestinthisway.

InWikipedia,autosuggestbeginswithalinktoadisambiguationpage,andthensuggestspagesrankedaccordingtopopularityinsearches.

Inthisexamplefromapropertymanagementcompany,autocompleteintheResourceszoneoftheintranetfirstsuggestsdirectlinkstotwodocumentsthathavethetermintheirtitles,andthenprovideslinkstotaxonomytermsfroma“businessactivities”facetcontainingtheterm.Clickingonthedocumentlinkswillbringtheusertothedocumentprofile,andclickingonthetaxonomytopicswillbringtheusertoasearchresultspageforthattopic.

Inthesamecompany,autocompletefromaquerystartingwith“design”intheStaffDirectoryzoneoftheintranetworksdifferently.Itsuggestsexpertiseareasfromthe“Expertise”facetofthetaxonomy,andalsosuggestspeoplewhohavebeentaggedwithexpertiseassociatedwith“design”.Thecontextofsearchchangesthewayautocompleteworks.

2.4 Dependencies Theeffectivenessofautocompletedependsonagoodunderstandingofusers’typicaldesiredoutcomeswhenenteringqueries.Thiscanbedeterminedfromthecontextfromwhichthesearchisbeinginitiated(theinterfaceIamusingknowsIamsearchingforbooks,orpeople,oranswers),orpredictivelyfrompopularsearches,orusingtheuser’sownpastsearchqueryprofile.

Taxonomycanbeusedproductivelyinautocompletewherethereisavarietyofknownsynonymsoracronymsinuseforcommonconceptsinthetaxonomy.Thesecanbecapturedinathesaurus,suggestedinthedropdownbox,andifclicked,canbringtheusertothesearchresultspagefortheassociatedtaxonomyconcept.

2.5 Potential Application Ingeneralautocomplete/autosuggestisdesignedtohelpusersseewhatcontrolledlanguageisavailabletothem,andtonudgeuserstowardsgreaterconsistencyoflanguageinconductingsearch,orinapplyingtaxonomytagstodocuments,whileallowingthemtheflexibilitytosearchontheirownterms.Whencombinedwithsynonymsthiscouldhelptomitigatethediversityoflanguageusedinyourorganisation.Combinedwithsearchzones,autosuggestcantargettermsuggestionsbasedontheknownfunctionsandcontextsofspecificusers.Moretechnicalstaffcouldhaveaccesstomoretechnicalvocabulariesintheautosuggestrulesconfiguredfortheirsearchzones.

PATTERN 3. BEST BETS

3.1 About This Pattern Forsomesearches,ifyouknowthequeryterm,thenyouwillbeabletodeterminethebestpossibleresourcestomeettheneedexpressedbythatquery.InBestBetsyoumanuallywriterulesinthesearchenginetotellittoforce-rankcertaincontentitemsatthetopofaresultspageforagivenquery.

3.2 Benefits Thispatternensuresthatuserswillseethemostauthoritativeandhelpfulresourcesfirst,atthetopofasearchresultspage.Bestbetscansharpenthequalityofsearchresultsanddirectuserstothemostauthoritativecontenttosupporttheirwork.Concentratingon,andfacilitatingfrequentqueriesthroughbestbetscanshowhighvalueintermsofuserappreciationofsearchveryquickly.

3.3 Illustrations

TheBBCwebsiteuses“bestbets”toprioritizegeneraloverviewarticlesforagiventopicatthetopoftheresultspageforitsgeneralsearchacrossallzonesofthewebsite,evenifthoseresultsarequitedated.Withinthenewssectionofthewebsite,articlesareorderedbyrecency.

Bestbetsareoftenconfiguredonadocumentlevel.However,theycanalsobeconfiguredtowardsataxonomyconcept–foragivenkeywordsearch,thesystemcanreturnthe“best”resultsforthenearesttaxonomytopicforthatsearch,evenifthetaxonomytopicitselfwasnotused–e.g.ifasynonymwasused.Inagovernmentagency,“procurement”wasacomplexareawithseveraldifferentkindsofprocurementproject,andeachprojecthaditsownprojectstages,templates,anddocumenttypes.Inasearchontheintranetfor“procurement”(bykeywordortaxonomysearch)thesearchresultspageshowedalinktoa“ProcurementHub”pageatthetopofthesearchresults.Navigatingtothispageallowedtheusertoconductaspecializedsearchorbrowseoperationusingprocurementfacetsandcategoriesthatwouldhavebeentoocomplextoshowgeneralusersinthegeneralsearchinterface.HowevertheuserisnotforcedtogototheHub.Theirsearchresultspageshowsotherprocurement-relatedresourcesthattreatprocurementatamoregenerallevel(suchasprocurementpolicies).

3.4 Dependencies Bestbetsdependonregularreviewofsearchlogstodeterminetopqueries.Sometimestheintendedtargetofthosequeriescanbeseeninthesearchanalytics(forexample,whichitemsaremostfrequentlyclickedthroughfromthesearchresultspage),sometimesunderstandingthequeryandwhatisbeingsoughtrequires

someinvestigativeworktodeterminethebestpossibleresourcestobeassociatedwithit.Taxonomycansupport“bestbets”byassociatingapreferredrankedsetofsearchresultstogiventaxonomyconceptsortheirsynonyms.Thisisusefulifyoucanpredictfromagiventaxonomyconceptwhichsetofresourceswouldbemostusefultodisplayfirst.Associatingbestbetswithtaxonomyconcepts(andtheirsynonyms)ismorescalablethanwithindividualqueriessincebestbetsdependonmanualrule-writingtoassociatespecificquerieswithspecificresources.

3.5 Potential Application

Asthecontentbasegrowswithinyourinformationsharingplatforms,bestbetswillbecomemoreimportanttodirectusersquicklytowardswhatislikelytobethemostrelevantandusefulresultforagivenquery–especiallyifyouarebuildingintegratingresourcepagesortopicpagestohelpstaffnavigatecomplexknowledgedomains.Howeveritdoesdependheavilyondetaileduserknowledge,andonasearchenginewithanactivesearchanalyticsfunctioninplace.

PATTERN 4. FACETED NAVIGATION

4.1 About This Pattern Facetednavigationallowsausertobeginasearchwithakeyword,andthenrefinetheirsearchusingtaxonomyfacetsasfilters.It’salsoknownasprogressivesearch.

4.2 Benefits Theprimarybenefitoffacetednavigationisthatitallowsuserstoreducetheirsearchresultsdowntoamanageablenumber,basedonfactorsthatareofmostinteresttothem,representedintaxonomyfacets.Differentusershavedifferentfactorsofinterest,andeachtaxonomyfacetshouldrepresentawayofapproachingthecontentthatisofinteresttosomeusersandnotothers.Othermetadataelementssuchastimeperiod,priceorlocationcanalsobeusedasfilters.Forexample,afactorofinterestforsomeusersinbuyingacarmightbewhetheritisautomaticormanual,forotherswhetheritisdiesel,gasoline,hybridorelectric,orforotherswhetheritisacertainbrand.Facetsallowuserstorefinetheirsearchesquicklybasedonwhatisimportanttothemandtheyallowthesameinterfacetosupporttheneedsofmultipletypesofusers.

4.3 Illustrations Forresterresearchusesfacetsandadditionalmetadatatohelpusersnavigateitsresearchproducts.Resultscanberefinedusingmetadataelementsfordaterange,vendororanalyst.TaxonomyfacetsusedasfiltersincludeContentType,PrimaryRole,Methodology,Industry,Topics,MarketImperatives,andRegion.Forresterincludetwousabilityfeaturesintheirfilters:

1. Theyonlyshowtopicsthathavecontentitemsassignedtothemandthetagitselfshowsthenumberofcontentitemspresent.Topicsthatexistinafacetbutdon’thavecontentitemsassignedtothem,arenotdisplayedintheinterface.Thisavoidstheproblemof“falsedrops”whereauserclicksonatopiconlytofindtherearenoitemsthere.

2. Whereatopiclistislongerthanfiveitems,theyonlyshowthetopfiveitems,andadda“Showall”linktoindicatetotheuserthattheycanexpandthelistiftheywish.Thisensuresthatallthefilterscanbereadilydisplayedinthesamewindow.

Inagoodfacetednavigation,itshouldbepossibletoreviewthesequenceofchoicesyouhavemade,andre-expandyoursearchifyoufindthatyouhaveoverlynarrowedyourpreferencesandneedtoexpandthemagain.IntheForresterexamplebelow,IcanseethetagsIhaveselectedsofar(redarrow),andIcanremovethembyclickingonthe“X”tore-expandmysearchagain.

Itisnotalwaysuser-friendlytoshowallthefacetsandmetadatafiltersatonce.Insomecasesyoumayknowfromuserresearchthatsomefacetsare“primary”facetsthatuserswilltypicallybegintheirsearcheswith,andsomefacetsaremorelikelytobeneededonlyasfollow-throughsontheleadfacets.Thepreviouschoiceprovidesacontextfordeterminingwhichsubsequentfacetsneedtobepresented.OntheArtistrising.comwebsiteIcanbrowsebyaSubjectfacetorsearchbykeyword.OnceIstartsearching,forexample,withthekeyword“horse”otherfiltersappear:Subject,Medium,Style,Tags,andColor.

Eachdecisionthatismadebytheuseristakenasinputtodesignthenextinterfacethatwouldbemostappropriatetotheirdecision.Whatweknowabouttheusercontextmayalsodeterminewhichfacetsandwhichfacetsequencestopresent.Inataxagency,“businessactivities”,“documenttypes”and“taxtypes”wereimportantfacetstoorganizeandtagthecontent.However,forofficersinthecorporatetaxdivision,“taxtypes”wasnotpresentedtothemintheirdepartmenthomepagesince“corporatetax”wasalreadyunderstoodasabackgroundfilteronwhattheywouldsee,andonlythebusinessactivitiesandcontenttypesthatwererelevanttocorporatetaxwerepresentedintheirfilters.Userswereonlyexposedtothefullsetoffacetsinthegeneralresourcessearchpage.

4.4 Dependencies Facetednavigationdependsongooduserneedsanalysistoidentifywhichfacetsareimportanttowhichusers,inwhichcontexts.Thisneedsanalysiswillalsohelptoidentifytheothermetadataelementsthatcanserveasusefulfilters.Thenthereisaneedtoensurethatcontentisappropriatelytaggedwiththemetadata.Youneedtobeabletoconfigurewhichfacet-filtersarepresentedtowhichusersinwhichcontexts.ThisfeaturecanbecombinedwithSearchZones.Forcontext-drivenpresentationoffilters(asinataxagency)orprogressivefilteringontheartistrising.commodel,wherefilteringinterfaceschangeasdecisionsaremade,thentheuserdecisionpathwaysneedtobemodeledcarefullyandthentestediterativelywithuserstoensurethattheinterfacesdoinfactsupportthetypicalwaystheywouldworkanddonotoverlyrestricttheirchoices.Interfacesalsoneedtorecordclearlytousersthechoicestheyhavemade,andbereversible,incasetheywanttoexpandorchangetheirsearchstrategy.

4.5 Potential Application Thediversityofcontenttypes,worktypesandthediversityofusersinlargecomplexorganisationsrequiresafacetedapproachtotaxonomyinsupportofsearch.Facetednavigationandprogressivefilteringisanaturalconsequence.Thereisrelativelylimitedrealestatetodisplayfacetsorothermetadatafiltersatanygiventime.Thereisanopportunitytocombinefacetednavigationandprogressivefilteringwithsearchzones,toshowfacetsandfiltersthatarelikelytobemostusefultoagivenuserdependingontheirfunction,startingpoint,andastheymakeprogressivedecisions.Inlinewiththeusabilityconsiderationsnotedabove,allfilteringdecisionsneedtobevisibletotheuserandreversible.

PATTERN 5. RICH SNIPPETS

5.1 About This Pattern Richsnippetsworklikeindexcardsthatshowthekeysummaryinformationforthecontentiteminthesearchresultspage.Theyusethewebpagemarkupormetadatatoshowthekeyinformationelementsabouttheitemthatwillhelpauserdecidewhetherthatitemisworthclickingthroughto.Forexample,resultsformoviesmayshowtheaggregatemoviereviewrating.Resultsforproductsmayshowasmallimagealongwithname,summarydescriptionandprice.

5.2 Benefits Thispatternisasignificantimprovementonseeingjust“topline”informationorthefirstfewwordsinadocument.Ithelpsusersmakeaninformeddecisiononwhethertheitemislikelytomeettheirneeds,withoutforcingthemtoclickthroughtotheitem.Itprovidesaconsistentwayofpresentingsummaryinformationaboutknowncontenttypes,andhelpsusersscanasearchresultspagequicklyandidentifytheitemsthataremostlikelytomeettheirneeds.Becausethesnippet’sinformationcontentispresentinstandardizedcontentmarkupormetadata,snippetscanbeportedintomultipleenvironmentsandplatforms(suchasmobile)inflexibleways.Theycanalsobecustomizedforcertaintypesofuserorenvironment.

5.3 Illustrations RichsnippetswereintroducedbyGooglein2009,initiallyforreviewsandpeople,thenforeventsandvideos.Theyhavebecomeastandardsearchresultspresentationtoolforcontrolledcontenttypes.Herearesomeexamples.

InSeptember2016,Googlereleasedanexperimentalmarkupforsciencedatasets,basedontheschema.org/Datasetclass.Thisallowskeyinformationaboutthedatasettobegatheredintoastructuredmarkupsothatitcanbepresentedinarichsearchsnippettodescribeanygivendataset,withoutrequiringuserstonavigatetoadatasetsite,andtrytofindallthosedetailsbybrowsingthesite.HereisanexampleofthebasicmarkupfordatasetsreleasedbyGoogle:

https://developers.google.com/search/docs/data-types/datasets Themarkupisdesignedtocover:

• tablesorCSVfilescontainingdata;• filesinproprietaryformatscontainingdata;• collectionsoffilesthattogetherconstitutesomemeaningfuldataset;• structuredobjectswithdatainsomeotherformatthatusersmightwantto

loadintoaspecialtoolforprocessing;and• imagescapturingthedata.

5.4 Dependencies Richsnippetsdependonbothuserandcontentanalysis.Userneedsanalysisisrequiredtodiscoverwhichelementsofinformationwouldbemostusefultomostusersinrelationtoeachcontenttype.Thesnippet(orindexcard)haslimitedrealestate,sodecidingwhatgoesonitrequirescarefulbalancing.Thecontentitselfneedstobemodeledtoidentifytheelementsofmarkupormetadatathatneedtobepresentandassociatedwiththecontentconsistentlyforthesnippetstowork.Contentmodelingisatechniqueforanalyzingthestructureandusesofdifferentcontenttypes.Itisusedtoidentifysalientpartsofthecontent,whousesitandhow.Fromthis,inferencescanbedrawnaboutmetadatastructure,taxonomyfacets,workflows,andsearchexposurestrategies.Becausethereiseffortand

standardizationinvolved,richsnippetsaretypicallyleveragedforwelldefinedandcommonlyusedcontenttypes.

5.5 Potential Application Searchanddiscoveryfordocumentsorothercontentitemsisoftenlinkedtoaneedtoaccesspeople(e.g.expertsinatopicoractivityarea),systemscontainingrelevantdata,eventsordiscussionsrelatedtothetopic.Searchsnippetsforpeoplewouldbedifferentfromsearchsnippetsforeventsordocumentsorresourcepagesorsoftwaresystems.Eachcontenttypewouldneedtobemodeledandvalidatedagainstuserneedstodesignrichsearchsnippets,andenhancethesearchexperience.

PATTERN 6. FROM SEARCH TO ACTION

6.1 About This Pattern Inthe“searchtoaction”pattern,actionbuttonsorlinksareplacedinthesearchsnippetontheresultspage,sothatuserscanchoosetoproceeddirectlytotheirintendedactionwithouthavingtonavigatetothedetailpage.Typicalactionsmightbeprint,save,bookmark,share,buy,review,watchpreviews,markeventsintoourcalendars,emailcontactsorrequestaccess.

6.2 Benefits Searchesareundertakenforavarietyofreasons,notallofthemsimplytoaccessinformation.Inmanycasessearchissimplyastepping-stonetocompletionofanintendedaction.Oftentheintendedactioncanbeinferredfromthenatureofthesearch,orthecontextofthesearch(wherethesearchtakesplace).Forexample,somebodybrowsinganecommercesiteisverylikelytowanttobuysomething.Byacceleratingthepathwaytoanintendedaction,thesearchfunctionalitybecomesmoreuseful.Monitoringtheactionstakenbyuserscanalsoprovideusefulfeedbackonthecontentandlayoutofrichsearchsnippets(Pattern5).

6.3 Illustrations Thee-commercesiteAlibaba.comhasseveralactionitemsontherichsearchsnippet.Theusercan:

• addanitemtoabasketandthencomparethefeaturesoftheselecteditemsinabasket(Compare)

• viewsimilarproductsinadropdownsetofcards(SimilarProducts)• contactthesupplierviaamessage• openachatwindowwiththesupplier

Tripadvisorallowsuserstoentercheckinandcheckoutdatesandcheckpricesforhotelsdirectlyfromtherichsearchsnippet.

AtWine.comclickingonthenumberofstarsonthesnippetforareviewedwinewillproduceapopuplinktothereviewsbutalsoanoptiontoimmediatelyprovideareviewofthewine.

IntheStaffDirectoryexampleshownearlier,eachrichsnippetforastaffmemberhasa“Follow”buttonwhichallowsuserstobenotifiedintheirnewsfeedofallfuturepostsbythatperson.Inprinciple,itwouldbepossibletoinitiateaninstantmessagingconversationfromthisinterfaceaswell.

6.4 Dependencies Richsnippetshavelimitedrealestate,astheyarestillcompiledtothesearchresultspageasasearchofmultiplesearchresults.Effective“searchtoaction”designdependsonhavingagoodunderstandingofthecommonandimportanttasksoractionsthattypicallyfollowanymajorcontenttype,andthenfacilitatingthoseactionsdirectlyfromthesearchsnippetitself.Thisrequiresuserneedsanalysis,testingandmonitoringviasearchanalyticstoseehowmanytimesactionbuttonsareactuallyused.

6.5 Potential Application Potentialactionswouldvaryaccordingtocontenttype.Forsoftwaresystems,anactionmightbe“Access”whichgivesaccessiftheuserispermittedto,andleadsthemtoa“requestaccessfunction”iftheydonothavepermission.Anotheractionmightbe“Bookmark”forcommonlyreferenceddocuments.Forpeople,anactionmightbe“Contact”,or“SeeContributions”,“SeeCollaborators”or“Seetopicstheyposton”.Fordocuments,actionsmightbe“View”,“RequestAccess”,“Bookmark”.

PATTERN 7. SUGGESTIONS / SEARCH EXPANSION

7.1 About This Pattern Searchenginesgatheralotofdataaboutassociationsbetweenpeopleandcontent,andbetweencontentandcontent.Frequentassociationscanbeturnedintosuggestions.Amazon’s“peoplewhoboughtthisbookalsobought…”isagoodexample.Taxonomiesalsomaprelationshipsbetweenconcepts.Apartfromhierarchicalparent-childrelationships,theycanmap“relatedterm”(RT)relationshipsbetweentermsinthesamefacet,oracrossdifferentfacets.Hence,ifatermisusedinsearchthathasRTrelationshipsthenthesearchenginecanalsosuggestadditionalresultsthatmayberelatedtotheuser’senquiry.

7.2 Benefits Whetherbasedoninferencesfromstatisticalassociationsbetweenpeopleandcontent,orbetweencontentandcontent,orbasedonknownrelationshipsbetweentaxonomyconcepts,thesearchsuggestion/searchexpansionfeaturecanprovideanimportantdiscoveryfunctionforusers,andleadthemtousefulcontentthattheyhadnotthoughttosearchforexplicitly.

7.3 Illustrations TheWine.comwebsiteshowswineswhosedetailswereviewedbyothercustomersforagivensearchquery.Thisprovidesaformofrapidfilteringforalongsearchresultspage.

Alibaba.com’s“Similarproducts”actiontabopensupanindentedlistofrelatedproductstotheonebeingviewed,basedontaxonomyandmetadata.

Theartistrising.comwebsiteshowsotherworksbytherelevantartistwhenyoulookatanartwork’sdetailpage.

Inalanddevelopmentagency,thetaxonomyhas“relatedterm”relationshipsmappedbetweenActivityFacet:Landreclamation,DocumentTypesFacet:Hydrodynamicstudyreport,EntityFacet:Marineengineeringconsultant.Thisallowsthesearchenginetogivetheusertheoptiontoexpandtheirsearchonanyoneofthoseitems,toresultsrelatingtotheothertwo.Thiscanbeveryusefulwherethepeoplesearchingfordocumentstendforexampleonlytothinkaboutdocumenttype.TheRTrelationshipwillpointtoresourcesthathavebeentaggedbyotherfacets.Thesamegoesforenhancingthequalityoftagging–withanRTrelationship,userscanbepromptedtothinkofaddingothertagsthatmayberelevanttotheirresource.

7.4 Dependencies Aswithmanyoftheothersearchpatternsdescribedinthisreport,goodsuggestionsoftendependonadetailedknowledgeofdifferentuserneeds.Agoodknowledgeofthestructureofadomainisalsouseful,sinceknownconceptualrelationshipscanbebuiltintotheunderlyingtaxonomyorontology,sothatrelatedresourcescanbesuggestedforanygivenconcept.

7.5 Potential Application Potentialsuggestionmechanismswithinyourorganisationmightinclude:

• Whichdocumentsorpagesaremostfrequentlyconsultedinthesameusersessionastheonecurrentlybeingviewedbyaspecificuser?

• Whichusersmostfrequentlyaccessthiscontenttype/document,andwhatothercontenttypes/documentsdotheyconsult?

• Whichusersmostfrequentlydiscussorciteorcommentonthiscontentitem,andwhatothercontentitemsdotheydiscuss?

• Whichtermsarerelatedinthetaxonomytothetermscurrentlybeingusedinasearchoperation,andwhatarethepeople,dataandcontentresourcesassociatedwiththoserelatedterms?

PATTERN 8. CONTEXT BUILDING

8.1 About This Pattern Severalofthesearchpatternswehavelookedatsofardependontheabilitytomakeinferencesaboutauser’scontextandneedsandthendirectthesearchinspecificways.Thecontextbuildingsearchpatternusesanumberofmechanismstounderstandusercontexts,oftenincombinationwitheachother:

• Queryanalytics–thesearchqueryisanalyzedforlinguisticfeaturesthatindicatethenatureoftheenquirysothatthesearchcanbenarrowedtospecificcontenttypesorzones–forexample,time/dateindicators,questionwords(e.g.who/whatwhen),currencymarkers,placenames,etc.

• User-suppliedchoices–theuserisinvitedtoidentifyatasktheyareengagedin,orchoosespecificsearchzonesorcontenttypesfortheirsearch,orsetuppersonalpreferences

• Automaticallyinferredcontext–fromknownuserrolesorfunctions,orsearchactivityhistory,orratingactivitysuchas“likes”orbookmarks,orcalendaredactivities.

• Contextualmobilesearch–usingtimeofday,geolocation,socialactivity.

8.2 Benefits Thispatternabstractsfromuserneedsanalysistoreasonableinferencesaboutcommonsetsofcontextualneeds.Itreducestheburdenofuserneedsanalysisinthefieldandscalestheactivityrequiredforanalyzingneedstoproducingremarkableimprovementsintargetedsearchforwideraudiences.

8.3 Illustrations Googleusesqueryanalyticstodetectcurrencyconversionqueries.Aquery“100sgdtousd”automaticallyproducesarichsnippetwithtoday’sinformationplusadditionalinformationabouttheexchangeratetrendsthatusersmightreasonablybecuriousabout,includingawaytogettopastexchangerates.Thisisbeforethegeneralresultslisting,bringingthesearchertoafactualanswerevenbeforethesearchresultspageloads.

WesawontheBBCnewswebsitethatusersareaskedtoidentifywhichareaofnewstheyweremostinterestedin,andnarrowdownthesearchzonesmostrelevanttothem,beforeconductingtheirquery.InGooglesearch,usersareabletodirectthesamesearchquerytowardsspecificcontenttypes,suchasmaps,images,videosandnews.

Inaprojectmanagementsystem,whenanewprojectteamsiteissetup,theprojecttypeisselectedfromthetaxonomybytheprojectmanager.Thisallowsthesystem(usingsearch)toautomaticallypopulatethesitewiththerelevanttemplates,specializedvocabulariesforthatproject,andpreviouslessonslearntrelatingtoprojectsofthattype.

Intheexamplebelow,asearchenginehasdetectedthatanemployeehasonlyrecentlyjoinedthecompany.AsearchforaMinutesofMeetingtemplateproducesaresultthatisdesignedtobehelpfultonewemployees.

Inamanufacturingfacility,lessonslearntfromsafetyincidentsrelatingtoplantequipmentarekeptonadatabase,whichislinkedtoanapponemployeemobiledevices.Wheneveremployeesareapproachingalocationwheretherehasbeenapreviousincident,thesystemsendsthemaproactivealertwithamessageandlinktothedetailsofthesafetyincident.

8.4 Dependencies Context-buildingpatternsinvolvecustomizedsearchfunctionalitiesdirectedatspecificneedsandcontexts.Hencetheutilityofthispatternislimitedtocontextsandneedsthatwillscale–wheretheinvestmentinradicallyimprovingsearchfunctionalityismatchedbythebenefitsitwillbring.Thismeanshighfrequencyorhighimpactsituations.

8.5 Potential Application Potentialcontext-buildingpatternswithinyourorganisationmightinclude:

• Queryanalytics–thesearchquerycanbeanalyzedforlinguisticfeaturesthatindicatethelevelofgranularityofthequery,whetheritisadocumentlevelquery(e.g.“…report”),aquerythatindicatesa“searchzone”ofa

broadertopicarea(e.g.“cleantechnology”),oraverybroadquerythatrequiresamoretraditionalbrowseandfacetedfilteringinterface.

• User-suppliedchoices–theusercouldbeinvitedtoidentifyataskfromaknownlistthatwouldusefullyhelptotargetthesearchatcontenttypesorsearchzones;userrolesanddepartmentaffiliationcouldbeusedtoproducecustomstartinginterfacesandstartingfacets;userscouldbeallowedtosaveandre-usecommonsearches(e.g.forcommontaskstheyengagein);

• Automaticallyinferredcontext–searchinterfacescouldbedesignedandconfiguredtoberesidentwithinusers’normalworkspaces,andtomakecallsonthelargercontentmanagementsystemsfromthoseworkspaces,carryingwiththemtheknowncontextualinformationaboutthatworkspace;“likes”orbookmarks,orpreviousaccessrequestscouldbeusedtomakesearchexpansionsuggestions,alongwithRTrelatedtermrelationships.

• Contextualmobilesearch–usingknownorganisationalactivitycyclesassociatedwithspecificdocuments,totweakthe“bestbets”–e.g.budgetandworkplanningcyclestoreturntemplatesandprioryeardocumentstothetopofsearchresultspages.

PATTERN 9. HIGHLIGHTING

9.1 About This Pattern Highlightingisaverysimplesearchpatternwheretermmatchesbetweenthequerytermandthetargetcontentaresnippedandhighlightedsothattheusercanseethekeywordsincontextandmakeanassessmentastowhetherthecontentcontainstherelevantinformation.

9.2 Benefits Thispatternhelpsuserstoscanlargeamountsofcontentquicklyandeither(a)makeanassessmentontheusefulnessofthecontentor(b)findthemostrelevantinformationwithinalargecontentresourceveryquickly.

9.3 Illustrations MS Word uses simple highlighting in document search, allowing the user to skip quickly through all occurences of a term in a document.

GoogleBookspresentssnippetscontainingthesearchtermshighlightedincontext,fromanygivenbook,allowingtheusertoquicklylocateaconcept,identifythepassagewhereitisbestdescribed,and/ormakeadecisionastowhethertobuythebook.

9.4 Dependencies Technically,thisisarelativelyeasypatterntoimplement.Howeverfromasearchinteractionpointofview,thesequenceofsearchinteractionsbycontenttypeneedstobecarefullymodeled.Thispatternworksbestwhenthereisalargeamountofcontenttobescannedquickly,soitdependsonbeingabletorecognizewhenasearchqueryrelatesspecificallytoapieceofcomplexcontentsuchasaresearchpaperormanualorpolicydocument.

9.5 Potential Application Thissearchpatternisprobablymostsuitedtosearchesrelatingspecificallytocomplexdocumentsorresearchproducts,tohelpusersdeterminewhetheragivendocumentisrelevanttotheirsearchneeds.Inthiscase,theGoogleBooksstrategyofshowinghighlightedkeywordsincontextwouldlikelybethemostuseful.

PATTERN 10. VISUALIZING RELATIONSHIPS AND INTENSITY

10.1 About This Pattern Searchresultscanalsobepresentedasvisualizations,andclickingondetailsinthesearchvisualizationscanleadtheusertodetailedsearchresults.Inavisualization,proximity,colorcodingandexplicitlinkagescanbeusedtosuggestrelatedresources,ordisplaytheintensityofresourcesoractivityinanenvironment.

10.2 Benefits Visualizationscanhelpuserstoseeinterestingassociationsbetweenresources,orhotspotsofcontentoractivity,orgapsinintensityorcoverage,inawaythatsimpletext-basedlistsofsearchresultscannot.Theyareparticularlyamenabletosupportingbrowseanddiscoveryonlargeandcomplexresourcebases,althoughtheycanalsobeusedtogeneratespecificsearchqueriesandresults.

10.3 Illustrations InthefieldofscientometricsOlivierBeauchesnehasmappedscientificcollaborationsaroundtheworld.Hisvisualizationshowsparticularhotspotsofcollaboration.Thisvisualizationisnotlinkedtosearchbutinprincipleitcouldbe,ifclickingonanygivenconnectiongeneratedasearchquery.Thiscouldproducethebiodataofthecollaborators,andtheresearchproductsofthatcollaboration.http://www.openculture.com/2013/08/mapping-scientific-collaboration.html

In2010,theNewYorkTimespublishedaninteractivevisualizationofcollaborationsbetweendirectorsandactorsonmovies,basedontheIMDBmoviedatabase.Hoveringoveranyoftherelationshipsproducesarichsnippetgivingtherelevant

moviedetails,andclickingonthelinktakestheusertothesearchresultdetailpageonIMDB.com.Itisparticularlyeffectiveatcallingouttheintensityofrelationshipsbetweenspecificplayersinthemovieuniverse.http://www.nytimes.com/newsgraphics/2013/09/07/director-star-chart/

Music-map.comallowsuserstosearchforanartistandmakesuggestions:“ifyoulikethisartistyoumightalsolikemusicfrom…artist”.Theclosertheartistsaretothesearchterminthevisualization,themorelikelyitis(basedonlisteninganalytics)thattheuserwilllikethatmusic.Ifyouclickonanyoftheotherartistsinthemap,itwillre-orientateitselfaroundthenewlyselectedartist,andwillpresentthenewlysuggestedartiststoexplore.Thisvisualizationsupportsasearchsuggestionpattern,andisdesignedtoallowaninteractivebrowsinganddiscoveryexperience.

Foamtreebygetcarrotsearch.comisabrowsablejavascript-basedtreemapvisualizationtoolthatcanvisualizehierarchies,facetsandstatisticaldatatoshowintensityofactivityorpopulationofcontent.Whenconnectedtoasearchengineitcanprovideinnovativeandengagingwaystointeractwithataxonomyandaresourcebase.Thisvisualizationcouldsupportafacetednavigationsearchpattern.

10.4 Dependencies Datavisualizationtosupportsearchrequiresinformationrepresentationskillsaswellasproficiencyinthevariousvisualizationtools,andhowtoconnectthemtosearchenginestogeneratesearchqueries.

10.5 Potential Application Withinanyorganisation,datavisualizationmightbeausefulavenuetoexploreforvisualizing:

• associationsbetweenpeopleanddatasets,andpeopleandtopics• intensityofpopulationoftaxonomytagsbydatasetsorcontent(e.g.how

muchcontentforsubjecttopics,industries,institutiontypesetc)• intensityofuseofcertaintypesofcontent,oroftaxonomytpicsorsearch

queries.Whenconnectedtosearch,thevisualizationshouldallowuserstodrilldowntothesearchsnippets,themetadataorthecontentitselfbehindthevisualization.

REFERENCES Thispaperisinspiredbythefollowingresources:JefferyCallenderandPeterMorvilleSearchPatterns:DesignforDiscovery(O’Reilly,2010)http://searchpatterns.orgMaishNichani“Searchexperience”seriesofarticles(OlaSearch,2017)https://olasearch.com/articles

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