Bibliographic Classification in the Digital Age: Current

BibliographicClassificationintheDigitalAge:CurrentTrendsandFutureDirections

AsimUllah,ShahKhusro,and

IrfanUllah

INFORMATIONTECHNOLOGYANDLIBRARIES|SEPTEMBER2017

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ABSTRACT

BibliographicclassificationisamongthecoreactivitiesofLibrary&InformationSciencethatbringsorderandpropermanagementtotheholdingsofalibrary.Comparedtoprintedmedia,digitalcollectionspresentnumerouschallengesregardingtheirpreservation,curation,organizationandresourcediscovery&access.Therefore,truenativeperspectiveisneededtobeadoptedforbibliographicclassificationindigitalenvironments.Inthisresearcharticle,wehaveinvestigatedandreporteddifferentapproachestobibliographicclassificationofdigitalcollections.Thearticlealsocontributestwoevaluationframeworksthatevaluatetheexistingclassificationschemesandsystems.Thearticlepresentsabird’s-eyeviewforresearchersinreachingageneralizedandholisticapproachtowardsbibliographicclassificationresearch,wherenewresearchavenueshavebeenidentified.

INTRODUCTION

Classificationistheprimaryinstinctofhumanbeingsinarranging,understanding,andrelatingknowledgeartifacts.Bibliographicclassificationprovidesaframeworkforarrangingandorganizingknowledgeartifactspreservedintheformofbooks,magazines,newspapersandotherholdingstoexplorenewavenuesofknowledgemanagement.TodayseveralclassificationschemesareinuserangingfromconventionalclassificationschemesincludingLibraryofCongressClassification(LCC),DeweyDecimalClassification(DDC),ColonClassification(CC),andUniversalDecimalClassification(UDC)toclassificationfordigitalenvironmentsincludingAssociationforComputingMachinery(ACM)digitallibrary1,InstituteofElectricalandElectronicsEngineering(IEEE)digitallibrary2,andOnlineComputerLibraryCenter(OCLC)cooperativecatalogue3.

Besidesthedifficultiesthatlieindevisingaclassificationscheme(time-consumingandresource-consuming),itisrequiredthateithertheexistingschemesshouldberevisedandextendedoranewclassificationschemeshouldbedevised,whichcouldactasacommonplatformforrepresentingknowledgeartifactsbelongingtodifferentcontexts.Suchaclassificationschemeshouldalsoresolvethechallengesindigitalpreservationandcurationandsupporttheprecise

AsimUllah([email protected]),ShahKhusro([email protected]),andIrfanUllah([email protected])areresearchersattheDepartmentofComputerScience,UniversityofPeshawar,Peshawar,Pakistan. 1http://dl.acm.org/2http://ieeexplore.ieee.org/Xplore/home.jsp3https://www.oclc.org/

BIBLIOGRAPHICCLASSIFICATIONINTHEDIGITALAGE|ULLAH,KHUSRO,ANDULLAH|doi:10.6017/ital.v36i3.8930

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Andaccuratesearchandretrievalofdigitalcollections.Thefirststep,inthisconnection,istoproperlyanalyzeandevaluatetheexistingbibliographicclassificationschemesandtodigouttheirstrengthsandlimitationsinclassifyingdigitalcollectionsaccuratelyandappropriately.Therefore,theobjectivesofthisresearcharticleinclude:

• To investigate and evaluate the available approaches to bibliographic classification fromtheperspectiveofdevisingaclassificationschemethatcanactasacommonplatformforclassifyinganytypeofdigitalcollection.

• Todevise evaluation frameworks that compares the availablebibliographic classificationschemesandapproaches.

• Topresent issues,challenges,andresearchopportunities instate-of-the-artbibliographicclassificationresearch.

Therestofthepaperisorganizedas:Section2presentsthecurrenttrendsintheclassificationofdigitalcollections.Section3presentstwoevaluationframeworksforcomparingandevaluatingtheexistingsolutions.Section4presentsresearchchallengesandopportunitiesinbibliographicclassificationresearch.Finally,Section5concludesourdiscussion.Referencesarepresentedattheendofthepaper.

ClassifyingDigitalCollections–AMixedTrend

Thebibliographicclassificationhasbeenthefocusofseveralresearcherstoproperlyclassify,catalogue,anddescribedigitalcollections.Inthisregard,twoapproacheshavebeenadopted:theformersupportstheuseofconventionalclassificationschemesincludingCC,DDC,andLCCetc.,indescribingandclassifyingdigitaldocuments,whilethelatterrecommendsdevisingsomenewwaysofclassificationsuchasACM4computingclassification.However,inmostofthedigitalenvironments,amixedtrendhasbeenobserved,wherealongwithnewclassificationschemes,categorizationisalsousedasacomplementarysolution.Forexample,ACMpresentsitsownclassificationsystemaspoly-hierarchicalontologyindescribingComputerScienceliteratureandforusinginSemanticWebapplications.ACMhasreplacedits2008ACMclassificationsystemthatservesasde-factomodelfortheclassificationofComputerScienceliteraturebygivingvisualtopicdisplayalongwithsearchingservices.Itservesassemanticvocabularyforcategorizingconceptsandafoundationofcomputingdisciplines("The2012ACMComputingClassificationSystem,").Similarly,IEEEdigitallibrarycategorizesitsholdingsintodirectoriesperitsownrulesofcataloguingandcategorization.Itcategorizesarticlesandstandardsintoseveralsubjectareasandclustersdocumentsthroughyearofpublication,authornames,contenttype,affiliation,publicationtitle,publisher,countryofpublication,alphabets,numeralsandalphanumericvalues5.Thedocumentcollectioncanbenavigatedthroughcollectionnames,numberofdocuments,bytopicandInternationalClassificationforStandards(ICS).

4http://dl.acm.org5http://ieeexplore.ieee.org/browse/standards/ics/ieee/


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TheDMOZ6directoryisthelargesthumanmadedirectoryofwebpages.Sinceitsinceptionin1998,itcategorizes3,861,137websitesavailablein90languagesinto1,031,719categoriesandsub-categoriesby91,928editorsandvolunteers.Inaddition,ithasitsDMOZRDFdumpsavailableonLinkedOpenData(LOD)cloud.AccordingtotheWorldWideWebConsortium(W3C),LODenablesthedataintegrationandreasoningatalargescale("Linkeddata,").Itestablisheslinksamongdataenablingmachinesanduserstoexplorethewebofdataratherthanthewebofdocumentsalongwithfindingrelateddata(Berners-Lee,2006;Bizer,Heath,&Berners-Lee,2009).However,itlacksinsemanticsearch(meaningfulsearch),whichaffectstheprecisionandaccuracyinexploringtherequiredresources.Also,thecategories,underwhichthewebsitesarekept,areneededtoberevisedbecausetherecanbefacetedandintra-hierarchicallinksamongwebpages.Inaddition,thecontentmanagementneedstobeupgradedforupdatingthedirectorywithnewentriesandthewayitreviewsandcategorizeswebsites(Boykin,2016).

Institutionalrepositoriesusethemixedapproachtowardscreating,collectingandmanagingmetadataforprintedanddigitalcollectionsusingseveralsourcesincludingconventionalanddigital.Thismixedtrendintroduceschallengestothemetadatamanagers(Chapman,Reynolds,&Shreeves,2009).Todealwiththesechallenges,thesubjectclassificationsystemscanbeverybeneficialinprovidingWeb-orientedservicesincludingsearchingofcontentsthroughsearchpatterns,browsing,andcontentfilteringbysubjectarea.However,atthesametime,acognitiveoverloadrisesfortheauthorsanddepositorsoftheinstitutionalrepository(Cliff,2008)thatneedsfurtherattention.

Tohandletheinformationoverloadinretrievingdigitalcollections,severalcontrolledmethodshavebeenproposedintheliteraturerangingfrommanualtechniques(e.g.,webdirectories)toautomatictechniquesincludingclusteringandclassification.Severalclassificationschemesincludingsentimentandsubjectclassificationhavebeendevelopedforclassifying(andcategorizing)webpages.Classificationisusedinfocusedcrawling,searchingandrankingresults,andclassifyingqueries.Clusteringalsoclassifieswebresourcesbutitisslightlydifferentfromclassification,whichisbasedonarigidpredefinedtaxonomyandrulesforinterpretingthemeaningofclassificationorder.Ontheotherhand,clusteringshowsflexibilityinclassification(categorization)ofwebdocuments(Zhu,2011).However,amixedtrendhasbeenobserved,whereclassificationandcategorizationareintermingledtofacilitateorganization,description,exploration,andretrievalofdigitalcollections.

SemanticWebbringsmeaningfulconnectionsbetweenthewebofdatasothatnotonlyhumansbutmachinescanalsounderstandthecontentofdocumentstoretrievethemostintendedandrequireddocuments.Thiswayotherrelateddocumentscouldalsobeeasilyconnectedandretrieved(Berners-Lee,2006).Tounderstand,describe,andrelateconceptswithindocuments,ontologiesareused.Therefore,researchershavebeenworkingonbringingsemanticsthroughSemanticWebandrelatedtechnologiestoautomaticallyclassifydigitalcollections.Forexample,

6http://www.dmoz.org


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(Beghtol,1986)arguesthatsemanticaxismakessyntacticalclassificationstructuremoremeaningfulandprovidestheplatformfordevelopingrelationshipsamongknowledgeartifactsthroughseveralwarrantsinclassificationsystems.Similarly,classificationontologyisusedinautomaticclassification(Wijewickrema&Gamage,2013)tominimizetheambiguityinvocabulary.Toobtainasinglesubjectfortheinputdocument,severalweightfunctionsincludingthetermfrequency-inversedocumentfrequency(TF-IDF),andfilteringmethodsareapplied.

SemanticWebandLODtechnologieshavealsobeenusedindealingwithbibliographicdata.Forexample,BibBase7,abibliographicdatapublishingandmanagementtool(Xin,Hassanzadeh,Fritz,Sohrabi,&Miller,2013)publishesbibliographicdataontheuserwebsiteaccordingtoLODprinciples.However,thesearelimitedbecauseofthelackofinteroperabilityamongnativelanguageswhiletranslatingclassificationrecordsfromsourcelanguagetothetargetlanguage(Kwaśnik&Rubin,2003).

Theclassificationschemesarealsobeingconvertedintoontologies.(Giunchiglia,Marchese,&Zaihrayeu,2007)haveappliedreasoningcapabilitiesofOWLontologiestoclassificationschemes.Theseontologiesareusedasinterfacestohumanknowledgeformachineswhereasclassificationschemesareinterfacestoknowledgeforhumans.However,thereislimitedsupportavailableforcross-disciplinarysearchingandaccommodationformoreviewsandinterpretationsofknowledge(Albrechtsen,2000).Thesupervisedandunsupervisedmachinelearningtechniquesareusedforautomatictextclassification.SupervisedmachinelearningtechniquesusemodelsincludingmultinomialNaïveBayesmodel,andBernoullimodel(Manning,Raghavan,&Schütze,2008)forclassification.Yelton(2011)appliesprobabilisticclassificationofimportantwords(andthereforeofdocuments)especiallybyconsideringAmazon’sStatisticallyImprobablePhrases(SIPs)8andGooglephrasesearchinsideabook.Forsubjectanalysis,hementionssimplistic;content-based;andrequirements-basedmethodsintermsofunderstandingtextclassificationandmanipulationofbooks.TheWikipediapagestructuralhierarchyisexploitedinautomaticallyharvesting,classification,categorization,clustering,andmetadataenrichment(Yelton,2011).

InformationExtraction(IE)isalsoappliedinclassifyingbooksautomatically.Forexample,(Betts,Milosavljevic,&Oberlander,2007)useIEmethodsforautomaticlabelingofbooksusingLCCclassification.Theyusedbag-of-words(BOW)model,bag-of-named-entityrecognition(NER)model,generalizingnamedentities(GAZ)modelinautomatictextclassification.Toachievebetteraccuracy,theyalsocombinedtheresultsofthesemodels.Howeverautomaticclassificationmayleadtolimitedsearchandretrievalbecauseofthemissingsemanticsassociatedwithphrasesorkeywords.Toovercomethisissue,afundamentalandpracticaltheoreticalmodelofclassificationisrequired(Jones,1970).

7https://bibbase.org/8http://www.amazon.com/gp/search-inside/sipshelp.html


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Table1categorizesthebibliographicclassificationapproachesintothreebroadercategoriesnamely:theoreticalapproaches,practicalapproachesandapproachesusedindigitalenvironments.Theoreticallyresearchershavediscusseddifferentviewpointsforclassification,whereaswegetadifferentviewwhentheseschemesareappliedforclassification.Practically,thesyntacticstructureisvaluedbyusingfacetedandenumerativetechniques.IndigitalenvironmentsliketheWebanddigitallibraries,strictboundariesofclassificationareoftencompromisedbycategorization.

ApproachestoClassification

TechniquesUsed

TheoreticalApproaches

1. Biasness(Mai,2009)(Mai,2010)2. Subjectivityandobjectivity(Hjørland,2016)3. EpistemologicalandSemioticapproaches(Hjørland,2013)

(Lee,2012;Mai,2011)(Tennis,2008)4. Empiricism,Rationalism,HistoricismandPragmatism

(Hjørland,2013)5. Multidisciplinarityapproach(Beghtol,1998)6. Scientificapproaches(Hjørland,2008)7. Positivisticandpragmaticapproaches(Dousa,2009)(Mai,

2011)8. Interdisciplinaryandevidencebasedpracticeclassification

(Hjørland,2016)9. Socialandculturalcontext(J.-E.Mai,2004)10. Bytrackingtheuniverseofknowledge11. Universalorder(Smiraglia&VandenHeuvel,2011)12. Integrativelevelsinclassification(Dousa,2009)13. Literarywarrant(Rodriguez,1984)14. Educationwarrant(Hjørland,2007)(Beghtol,1986)15. Semanticwarrant(Beghtol,1986)16. Syntacticwarrant(Beghtol,1986)17. Domainandusersrequirements(Mai,2005)18. Pluralismandhumaninterpretations

PracticalApproaches

1. EnumerativeandFaceted(Batley,2014).2. GeneralPurposeapproach(Mai,2003)andSpecialPurpose

approach(Mancuso,1994)e.g.classificationschemesforgeneralclassesofknowledgeareasorforaspecialclassofknowledgearea.

3. Syntacticaxis(Beghtol,1986)(Beghtol,2001)4. Semanticaxis(Beghtol,1986)(Beghtol,2001)


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ClassificationinDigitalEnvironment

1. DocumentSimilarity(HammingdistanceandEuclideangeometricapproaches)(Losee,1993)

2. Fuzzyapproach(Jacob,2004)3. Clustering(Nizamani,Memon,&Wiil,2011)4. Categorization(Koshman,1993)5. TF-IDFweighting(Dorjietal.,2011)6. Unsupervisedmachinelearningtechniques(Joorabchi&

Mahdi,2011).(K-meanClustering,hierarchicalclustering)7. Supervisedmachinelearningtechniques(Wang,2009)

(MultinomialNaïveBAYES,Bernoullimodel,SupportVectorMachine,RandomForest,K-NNtechnique)

8. InformationExtractionmethods(Gilchrist,2015)9. Probabilistictextanddocumentclassification(Maron,Kuhns,

&Ray,1959)10. Ontologies(Campbell,2002)

Table1.Categorizationofapproachestowardsbibliographicclassification

EvaluatingClassificationSchemes&Approaches

InthisSection,wepresenttwoevaluationframeworkstocompareandevaluatetheexistingclassificationandcategorizationsystemsandwell-knownbibliographicclassificationontologies.WehavechosenCC,DDC,LCC,andUniversalDecimalClassification(UDC)onthebasisoftheirstructuralpropertiesandwideusagebothinconventionalanddigitallibraries("Subjectclassificationschemes,"2015)("LibraryofCongressClassification,"2014)("AboutUniversalDecimalClassification(UDC),")(Press,2002)(Encyclopedia,1August2014).Someofthesepropertiesinclude:citationandfillingorder;notationsexpressiveness;flexibilityinclassificationprinciples,rulesandnotations;coverageoftheknowledgeareas;classificationschedulesandnotationsstructure;notationsbrevityandsimplicity;notationsmnemonics;notationshospitality;scheduleswithupdateableandcomprehensivesubjectsorder;andknowledgecoverage(Batley,2014).TheUDC,LCC,andDDCareuniversal,multidisciplinary,andwidelyusedsystems(Koch&Day,1997),whereasCChastheseminalandinspirationalvalueforthefacetedstructureofthebibliographicclassification.Therefore,theevaluationframeworkmainlytargetstheseclassificationschemesasournaturalchoicefortheevaluationandcomparison.Similarly,weevaluateACM9,IEEE10andDMOZ11usingtheevaluationframeworkasthesearethewell-knownandwidelyuseddocumentclassification&categorizationsystemsforthedigitallibraries.Table2presents22metricsusedintheevaluationframework.Theseevaluationmetricsareextracted

9http://www.acm.org/about/class10http://www.ieee.org/about/today/at_a_glance.html?utm_source=mm_link&utm_campaign=iaa&utm_medium=ab&utm_term=at%20a%20glance11https://www.dmoz.org/docs/en/about.html


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fromtheexistingliterature(Kaosar,2008)(Painter,1974)(Encyclopedia,1August2014)(Buchanan,1979)(Kaosar,2008)(Painter,1974)(Encyclopedia,1August2014)(Kochetal.,1997)(Reiner,2008)(Gnoli,Merli,Pavan,Bernuzzi,&Priano,2008)(Francu,2007)(Chan,Intner,&Weihs,2016).Thesemetricsinclude:(i)structuralcomplexity;(ii)notationalbrevity;(iii)predefinedstructure;(iv)rulescomplexity;(v)theoreticallaws;(vi)mnemonics;(vii)hospitality;(viii)searchcomplexity;(ix)usability;(x)precisionandaccuracy;(xi)multilinguality;(xii)interoperability;(xiii)semanticsearch;(xiv)biasinsubjectrepresentation;(xv)enumerativestructure;(xvi)facetedstructure;(xvii)facetedsearch;(xviii)consistency;(xix)LODdatasets;(xx)LinkedOpenVocabularies(LOV)support;(xxi)platform;and(xxii)warrantsofclassification.Thesemetrics,theirneed,andtheiruseinratingsofclassificationsystemsarediscussedinthefollowingparagraphs.InTable2,thesebibliographicsystemsareevaluatedforthesemetrics.Theindicatorüshowsthepresenceofmetricvalue,ûindicatorrepresentsthatthesystemhasnoorminimalsupportforthementionedmetric,whereasandN/Aisusedfornotapplicable.Inaddition,eachclassificationsystemhasbeenevaluatedandratedbasedonthesemetrics(Table3),whereFigure1graphicallydemonstratestherankingsandratingsoftheseclassificationsystems.

SchemesMetrics

CC UDC DDC LCC ACM IEEE DMOZ

StructuralComplexity

ü ü û û û û û

NotationalBrevity

û û ü ü ü ü N/A

PredefinedStructure

ü û ü ü ü ü ü

RulesComplexity

ü ü û ü û û û

TheoreticalLaws

ü ü ü ü û û û

Mnemonics ü ü ü ü ü ü ûHospitality ü ü ü ü ü ü üSearchComplexity

ü ü û û û û ü

Usability ü ü ü ü ü ü ûAccuracyandPrecision

ü ü ü ü ü ü û

Multilinguality ü ü ü ü û û üInteroperability û ü ü ü ü ü ûSemanticsearch


Biasinrepresentation


EnumerativeStructure

û ü ü ü û û û


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FacetedStructure

û ü û û ü ü û

FacetedSearch û ü ü ü ü ü ûConsistency ü ü ü ü ü ü üLODDatasets û ü ü ü ü ü üLOVSupport û û û û ü û ûPlatform N/A UDC

consortiumOCLC Library

ofCongress

ACMdigitallibrary

IEEEXploredigitallibrary

OpenDirectoryProject

Warrantsofclassification

LiteraryWarrant(Giess,Wild,&McMahon,2007)

LiteraryWarrant(Perles,1995)

LiteraryandScientificWarrant(Giessetal.,2007)

LiteraryandScientificWarrant(Giessetal.,2007)

ScientificResearchwarrant

ScientificResearchwarrant

N/A

Table2.EvaluationofClassificationSchemes

Thestructuralcomplexitymeansdifficultiesinusingthestructureandnotationsinclassifyinganddescribingaspecificsubjectarea.Themetricwillhelpusinselectingaclassificationschemeorsystemthatiseasytouseinclassifyingdocumentcollectionbyrequiringshortnotationsandsimplerules.ThenotationsandrulesarecomplexinCCandUDC(Ranganathan,1968).Thiscomplexityisbecauseofthefacetedstructureintheseclassificationschemes(Sukhmaneva,1970).ThestructuralcomplexityofCCisgreaterthanthatofUDC.UDCcomesatsecondpositionincomplexityascomparedtoCC.Becauseofitsenumerativestructure,LCCstandsatthirdposition,asitislessercomplexthanCCandUDC.DDCisthesimplestinthislistbecauseitisbasedonenumerativeclassificationstructureandontheprincipleofdividinguniverseofknowledgeintodefinedclasses.IEEEismorecomplexthanACM,whereasDMOZistheleastcomplexsystem.Theclassificationsystemwithgreaterstructuralcomplexityisrankedlowerinthelist.Therefore,basedonthismetric,theclassificationsystemscanberankedasDMOZ,ACM,IEEE,DDC,LCC,UDC,andCC.

Thenotationalbrevitymeanshowbriefarethenotationsindescribingandunderstandingtheholdingswithminimumnumberofsymbolsandminimalcognitiveload.DDCuseswell-organizedshortnotationsandtheirmnemonicvalueisalsogreater(Comaromi&Satija,1983)(Hyman,1980).LCChasnotationalbrevity(Chanetal.,2016).UDCuseslengthynotations(Kaosar,2008)ascomparedtoDDC,whereasCCalsouseslengthyandcomplexnotations(Chatterjee,2016).ACMnotationsareshorterthanIEEE,whereasDMOZdonotuseanynotationsatall.Usingthismetric,theseclassificationsystemscanberankedasACM,IEEE,DDC,LCC,UDC,CC,andDMOZatthelastwithnousageofsymbolsatall.

Thepredefinedstructuremeansthattheclassificationschemefollowsrigidpre-assumedsubjectcategorizationalongwithclassificationclassmarks.Inthisregard,UDCandLCCareenumerative


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andimposesubjectivityviewpointofclassificationbyfollowingapredefinedstructure(Goh,Giess,McMahon,&Liu,2009).Beingfaceted,CCarrangesbasicconceptsinfewpredefinedcategories(Satija&Martínez-Ávila,2015).DDCalsohasthepredefinedhierarchicalstructureofclassification(Press,2002)(Jonassen,2004).Amongtheseschemes,CChasminimalpredefinedstructurebecauseofusingfacets;UDCisbothenumerativeandanalytico-synthetic.LCCisenumerativebutpossessesweakerpredefinedrulesforthestructuraldesign.Becauseoftherigidenumerativehierarchiesandpredefinedclassstructure,DDCcomesatfirstposition.DMOZhasthemostrigidpredefinedstructureascomparedtothatofIEEEandACM.Theclassificationsystemwithmostrigidandpredefinedstructureisrankedlower,andtherefore,therankingcouldbeCC,ACM,IEEE,UDC,DDC,LCCandDMOZ.

Thecomplexityinrulesdeterminesthedifficultylevelinapplyingclassificationrulesonknowledgeartifacts.CCpresentsacomplexsetofrulesandclassificationtheory,whichiscomparativelydifficulttoimplementandunderstand(Tennis,2011).LCCisalsocomplex("LibraryofCongressSubjectHeadings:Pre-vs.Post-CoordinationandRelatedIssues,"March15,2007)inimplementingLibraryofCongressSubjectHeadings(LCSH)inpre-coordinatedsubjectstrings.DDC’srulesandprinciplesarecomprehensiveandcomplete(Press,2002)andeasierthanthoseofCCandLCC.UDCisalsoeasytounderstandandimplement(Piros,2014).ACM,IEEE,andDMOZaresimpletouseandunderstand,andtherefore,bearsnosuchcomplexity.Aclassificationsystemwithgreatercomplexityisrankedlower,therefore,basedonthismetric,therankingscouldbeACM,IEEE,DMOZareontopwithsimilarrankingsfollowedbyUDC,DDC,LCC,andCC.

Theoreticallawsareconsideredasametrictoanalyzethefoundationsofclassificationsystemstounderstandwhethertheyarebasedoncertaintheoreticallawsandprinciplesofclassificationornot.UDCcombinestheenumerativeandfacetedapproachesgatheredfromDDCandCC(Kaosar,2008).ThesyntheticprincipleofUDCcontributestoitswidespreadusebutitisnotenoughattheintellectuallevelformakingtherelationsbetweenthesubjectfacets(Kyle&Vickery,1961).UDClacksstandardrulesforitsapplicationformakingfacets,buttherearerulesforitsstructuralrepresentation(McIlwaine,1997).Therefore,thestructuralandsyntheticrulesaregoodenoughforitsapplicabilitybutitshouldberefinedfurtherattheintellectuallevel.ThetheoreticallawsofCCarebasedonthefacetedapproachofmanagingknowledgeartifacts.CChassoundrulesandprinciples,whichincludedifferentpostulates,laws,principlesandcanons(Batley,2014)(ArashanapalaiNeelameghan&Parthasarathy,1997).Ontheotherhand,LCChasweakertheoreticalfoundations.Therealsoexistssomeintellectualandstructurallimitationsduetoitsenumerativestructure(SanSegundoManuel,2008).DDChasthehierarchicalandtheenumerativestructurewhichisbasedontheknowledgephilosophyofhierarchicaldivision(Hjorland,1999).Becauseofthestrongtheoreticalfoundations,CCisatthetopofthislist,DDCissecondbecauseofitsuniversaltheoryofknowledgedivision,UDCisthirdforbeingexploitingthetheoriesofDDCandCC,LCCisatfourthpositionforcomparativelyweaktheoryofclassification,whereasACM,IEEE,andDMOZpresentnoorverylimitedtheoreticallawsorphilosophicalrulesofclassification.


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Thesupportforusingmnemonicsenableshumanclassifierstoeasilymemorizethesymbolsandnotationsofclassificationscheme.ThesystematicandliteralmnemonicsareusedinUDC(Satija,2013)(Kaosar,2008).Themnemonicsareincreasedthroughmnemonicdevices,whicharedescribedthroughthecanonsofmnemonics(Kaula,1965).LCCusesliteralmnemonics(Satija,2013),whereasDDCusessystematicandliteralmnemonicsbutitssystematicmnemonicsarenotconsistent(Satija,2013).ThereareseveralseminalmnemonicsinCC(Rahman&Ranganathan,1962).ThesemnemonicdevicesincreasemnemonicsinCC,buttheformationandlengthofthenotationsaffectsthismnemonicquality.ACMhasgreatersupportformnemonicsincomparisonwithIEEE,whereasDMOZisthecollectionofwebpagesunderspecificcategories.Basedonthismetric,therankingsofclassificationsystemscouldbeDDC,UDC,LCC,ACM,IEEE,CC,whereasDMOZlacksinusinganymnemonicdevicesornotations.

Hospitalitymeanstheabilityofaclassificationschemetoincorporatenewknowledgeareasexpressedindifferentmultilingualcontexts.HospitalityispresentinUDC(Kaosar,2008).CCisalsohospitablefornewsubjects(DeGrolier,1962).LCCishospitableforexpressingthenewsubjectsandknowledgeareas(Satija,2013).DDCishospitablefornewsubjectareas(Satija,2013).Byapplyingthismetric,aclassificationschemewithfacetedapproachisnaturallymorehospitablethanothers.Therefore,CCismorehospitableandatthetopinthislistfollowedbyUDC.DDCisatthirdpositionforbeingfollowingenumerativeapproach.LCCisatfourthpositionbecauseofit’sofpureenumerativestructure.IEEEandACMareatfifthpositionbycoveringshortspanofknowledgeareas,facetedstructure,andefficientsearch.DMOZiscoveringonlywebpagesinalreadyspecifiedcategoriesthereforeitisatseventhposition.

Searchcomplexitymeasuresthedifficultyinsearchingartifactsusingaclassificationscheme.Itdescribesthatwhichclassificationschemeisworthinsearchingaspecificdocument.SearchcomplexityisminimalinUDCbecauseofitssyntactic-analyticoandenumerativenature(Kaosar,2008),whichcancontributeinsearchapplicationsinbothWebbasedandin-housesearchingapplicationse.g.,OnlinePublicAccessCatalog(OPAC).ThetheoryandphilosophyofCCisthetrendsetterfortheknowledgemanagement,resourcediscovery&access,however,accordingto(Raghavan,2016)searchingthroughCCiscomparativelyweakerthanotherbibliographicclassificationschemes.Accordingto(Chan,2000),LCCandLCSHhavethepotentialtoprovidetheeaseinsearchingbecauseofrichervocabularyforgreatersubjectcoverage,synonymandhomographcapabilities,pre-coordinatedsystem,browsingcapabilityinmulti-facetedstructure,multilingualsupportandMARCformatsupportwithsematicinteroperability.However,itislimitedinprovidingeaseinsearch&retrievalprocess,whichincludesyntaxandapplicationrulescomplexity,lackoftrainingforthepersonnel,andtoolengthyandcomplexsearchingstrings.DDCandLCCareaggregatedinClassify12projectinitiatedbyOCLC.WiththeuseoftheClassifyapplication,thesearchexperienceofthecatalogersandpatronsbecomesmucheasier.Usingthismetric,DDCstandsatthetopwithleastcomplexitythanLCC,UDC,andCC.IEEEismorecomplexthanACMandDMOZ.Theclassificationschemewithlesssearchcomplexitywillberankedhigher. 12http://www.oclc.org/research/themes/data-science/classify.html


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Therefore,ACMandIEEE,DDC,andLCCstandfirstwithleastsearchcomplexityfollowedbyUDC,andCC.DMOZstandsatthelastpositionwithgreatersearchcomplexityhavinglooseboundariesofcategorization.

Usabilityanalyzesthedifficultyinusingaclassificationschemeforclassifyingandsearchingdocuments.Thismetricdefinestheeaseoflearningandeffectiveusage.Usabilitymeasuresusersatisfaction,userunderstandingofthesystem,andprecisionwithminimalrecallinlesseramountoftime(Singapore,2016).OCLChasincludedstructuralchangestoimproveusabilityandsimplifyclassificationtasks("DeweyServices:DeweyDecimalClassificationSystem,").TheClassify13projectaimsatfindingbooksthroughawebinterface,whichiseasytouseandunderstandbyusingDDCandLCC.UDCisextensivelyusedinWeb-basedsearchandretrievalapplications(Kaosar,2008).Thisclassificationschemeisusedinseveralinstitutions’OPACsystems("LibraryOPACscontainingUDCcodes,").TheUDCnotationsaresupportivefortheusability(Slavic-Overfield,2005).However,theuserinterfaceoftheseOPACsearchsystemscouldbefurtherimproved(Slavic,2006)(Pollitt,1998)(Schallier,2005).CCisthesourceofinspirationandastandardizedmodelfortheusabilityoffacetedstructureofbibliographicclassificationintheelectronicandwebbasedenvironments(Thelwall,2009).In(Rosenfeld&Morville,2002),thephilosophyandmethodologyofCCisconsideredattheabstractandtheoreticallevel.ThisassessmentofCCleadsustotheargumentthatthefacetedstructureissupportiveinpreciseretrievalwithaconsiderablyhighcognitiveworkattheuserendascomparedtoDDCandLCCbecauseoftheirsimpleenumerativestructures.LibraryofCongressusesLCCinitscatalog14andClassificationWeb15applications.TheseapplicationsareexploitingLCSHsandLCCinuserfriendlymanner.Bylookingattheusabilityaspectoftheseclassificationschemes,therankingthroughthismetricsappearsasDDCisatthetopforitseasyenumerativestructureandnotationalsimplicityalongwitheasytouseWebapplications.LCCisatsecondpositionbecauseofitsenumerativestructureandadoptabilityinwebapplications.Beingenumerativeandfaceted,UDCstandsatthethirdposition.CCforbeingapurefacetedschemewithcomplexnotationsandrules,isrankedatthefourthposition.Similarly,IEEEandACMarefacetedandeasytouse,andtherefore,sharethefirstpositionwithDDC.DMOZwithlooseboundariesofcategorizationisleastusablewithlimitedbrowsingandsearch.

Theaccuracyandprecisionmetricmeasureshowaccurateandpreciseaclassificationsystemcanidentifytheexactlocationsoftheholdingsinthegivenknowledgespace.UDCshowsaccuracyandprecisioninfindingtherequiredknowledgeartifact(Kaosar,2008).TheaccuracyandprecisionofCCgetscompromisedasitslengthynotationsintroducescomplexityinsearchinganddiscoveringdocuments(Satija,2015).LCCandDDCwereresearchedforaccuracyandprecisionbyusingaprototypemodel(Gnoli,Pusterla,Bendiscioli,&Recinella,2016)forautomatictextclassificationofelectronicdocumentsusingclassificationmetadataoflibraryholdingsfromLCCandDDC

13http://classify.oclc.org/classify2/14https://catalog.loc.gov/vwebv/searchBasic15https://www.loc.gov/cds/classweb/classwebfeatures.html


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datasets.Itwasobservedthatforprecision,thereisaneedforincreasingDDCandLCCbibliographicdataontheWeb,introducingsearchingcapabilitiesforbibliographicdataatthemicrolevelofanydocument,andincreasingtheefficiencyofuserinterfacesfornavigationusingDDC-basedbrowsingstructure(Joorabchi&Mahdi,2009)(Joorabchi&Mahdi,2011).Therefore,CCbecauseofthepurefacetedapproachhashigh-levelprecisioninsearchandresourcediscovery.UDCstandssecondbecauseofbeingenumerativeandenumerativeandanalytico-synthetic.DDCisatthethirdpositionasOCLCmaintainsandupdatesitsstructureregularlyalongwithstate-of-the-artsearchapplications.LCCsharesthethirdpositionwithDDC,beingregularlyupdatedandmaintainedbyLibraryofCongressforprecisionintheirsearchapplication.IEEEandACMalsoshowgreaterprecisionintheirsearch&retrieval,andtherefore,sharethethirdpositionwithDDCandLCC.DMOZarethemanuallycreatedandupdatedcategoriesofwebpages,havinglimitedkeywordsearchwithverylowprecision.

Inconnectiontotheevaluationframework,multilingualitymeanstoclassifyanddescribetheknowledgeartifactswrittenandexpressedinvarietyofnaturallanguagesandtheavailabilityofanyclassificationschemeindifferentnaturallanguages.DMOZsupports72differentlanguagesoftheworldandthereforestaysatthetop.UDCismultilingualbysupportingFrench,Portuguese,SpanishandRussian(Slavic,2008)(Koch&Day,1997)andhasbeentranslatedintolanguages("UniversalDecimalClassificationsummary,"2017).LCCsupportsworksin19languagesubclasses("LibraryofCongressClassificationOutline:ClassP-LanguageandLiterature,")includingGerman,Slavic,OrientalLanguagesandRomanlanguagesetc.ThetranslationsofDDCsupporttolocalizethisschemefordifferentlanguagesoftheworld(Vizine-Goetz,2009).DDCistranslatedin30differentlanguagesbutcoversdifferentlanguagesinonlysevenclassesi.e.,from420to490classnumber("DeweyDecimalClassificationsummaries,").CCshowsminimalmultilingualsupportbecauseofitssub-continentalorigin(ANeelameghan&Lalitha,2013;Raghavan,2016).ACMandIEEEareinEnglishlanguagesonlyandtherefore,shownomultilingualityatall.Usingthismetric,wecanconcludethatDMOZisatthefirstposition,followedbyUDC,DDC,LCC,andthenCC.

Consistencymeasuresthelevelofuniformityinclassificationsystemtoclassifysubjects.Accordingto(Batty,1967),intheearlierstages,CCshowsnoconsistencybutbytheadditionofconsistencycannons,ithasgraduallybecomeconsistent.LCCseemslessconsistentinexpressingdifferentsubjectsareas(Madge,2011).DDCandLCCwerefoundshortofdefiningandclassifyingreligiousholdingsespeciallyJewishcontents.Theseschemesalsoshowbiasnesstowardsdifferentreligiousandregionalcontents(Maddaford&Briefing).AlthoughDDCisalittlebitinconsistent,stillitcanclassifycomplexsubjects(Gnolietal.,2016).UDCalsoshowsinconsistency,whichcanbesortedoutbyintroducingspecificUDCclassestodatabaseinonlinesystem(Kaosar,2008).DDCshowscomparativelygreaterconsistencyinclassifyingnewsubjectswithconstantuniformity;CCisrankedsecondbecauseoftheintroductionofcanonsofconsistency.LCCandUDCarerankedatthirdposition.Forbeingonlylimitedtothescientificresearcharticles,IEEE


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andACMareatfourthposition.DMOZstandsatfifthpositionduetoitslooseboundariesofcategorization.

Theinteroperabilitydetermineshowmuchagivenclassificationschemeisinteroperableinexpressingitsclassificationartifactswithotherschemes.UDCisinteroperable(Koch&Day,1997)andsupportsintegrationwithothersystems.CC,becauseofitssub-continentalorigin,showslimitedinteroperability(ANeelameghan&Lalitha,2013)(Raghavan,2016).LCCshowsinteroperabilitybybeingcapabletomapwithDDC(Vizine-Goetz,2009).TheinteroperabilityandmultilingualityofDDCenablesittomapwithotherclassificationschemes(Vizine-Goetz,2009).IEEE,ACMandDMOZdatasetsareinteroperablewithotherwebapplications.Basedonthismetric,DDC,LCC,UDC,ACM,IEEEandDMOZarestandingatfirstpositionbecauseofthepresenceoftheirinteroperabilityanddataharvestingprotocolsandontologiesinthedigitalenvironment.DMOZstandsatthesecondpositionbecauseoflimitedinteroperability.CCprovidesonlyphilosophicalandtheoreticalmodelbutwefoundnopracticalweb-basedapplicationsoitisnotincludedinthislist.

Byenablingsemanticsearch,aclassificationschemecanproactivelyrespondtoinformationseekersusingitsfacetedstructure.UDC,becauseofitssemanticstructure(Slavic,2008),containssemanticsearchcapability.TheclassificationtheoryandphilosophyofCCprovidesthebasisforclassificationontologydevelopment(Panigrahi&Prasad,2005),whichmakesobviousitscapabilityofsemanticsearchandinference.LCCsupportssemanticsearchthroughLODsupport,semanticallyenabledLCSHandauthoritycontrolfiles("LCLinkedDataService:AuthoritiesandVocabularies,")(Harper&Tillett,2007).DDCalsocontainssemanticfeatures(Green,2015),whichcanbeutilizedinthesemanticsearchapplications.Therefore,itcanbeconcludedthatsemanticsearchisalsosupportedbyDDC.Thismetriccanbebetteranalyzedinthedigitalenvironmentandespeciallythroughanalyzingthesebibliographicclassificationsfortheirontologies.LCCcouldberankedfirstbecauseofitsexpressiveontologywithefficientsemanticsearchapplication.DDCisatsecondposition,becauseofefficientsearchbutlimitedusageofitsontology.ACMisatthirdpositionbecauseofitsexpressiveontologyandefficientsearchbutlimitedcoveragetoscientificdomain.IEEEisatfourthpositionbecauseofitsfacetedsemanticsearch.UDCcomesatfifthpositionbecauseofitsontologicalpresencebutwithlimitedusage.CChasnoapplicationinthedigitalenvironment,whichcoulddemonstrateitscapabilityforsemanticsearch,althoughitprovidesthebasisforthesemanticlevelforallbibliographicclassificationsystems.DMOZlacksinsemanticsearch,whereitisonlybasedonkeywords.

Biasinsubjectrepresentationmeansinclinationfororagainstsomesubjectswhichresultsinunfair,partialnegligenceorfullyignoringanysubject.DDCandLCCarebiasedinrepresentingdifferentknowledgeandregionalinformation,e.g.,Anglo-Americanbias(Tomren,2003),whileUDCisbiasedtowardsEuropeanculture(Fandino,2008).CCisbiasedtowardsdifferentknowledgeareas(Satija&Singh,2010).Aclassificationsystemwithleastbiasnessisrankedhigher.Therefore,inthisconnection,DMOZisrankedhigherforshowingno/leastbiasness;CCisrankedsecondbecauseofthepresenceofacutebiasnessfollowedbyDDCshowingcomparatively


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lessbiasnesstowardsreligionandregionalsubjects.LCCcomesatthefourthpositionfollowedbyIEEEandACMthatshowgreaterbiasnesstowardscertaindomains.

Enumerativestructureexhibitstherigidhierarchies.LCCisenumerative(Gohetal.,2009;Perles,1995)(Bryant,October4,1993).UDCisnearlyenumerativeandfaceted(Kaosar,2008)(Bryant,October4,1993)andDDCisbothanalytico-syntheticandenumerative(Hallows,2014).CCisfaceted(Chatterjee,2016;Dawson,Brown,&Broughton,2006).Bycomparingthesesystems,LCCfullysupportsenumerativestructure,andthencomesDDC,whereasUDCisnearlyenumerativeandCCshowsnoenumerativestructureatall.LCCandDDCareenumerative.Thetrendistowardssemanticandfacetedstructure,andtherefore,enumerativestructureinclassificationsystemsisnotadesirablecharacteristic.Therefore,thesystemwithenumerativenaturewillberankedlower.Basedonthismetric,CCandDMOZareleastenumerativeandtherefore,rankedhigher,followedbyIEEEandACMatthesecondposition,thenUDCatthethirdposition,whileDDCandLCCatthelast.

Thefacetedstructuremeansthesemanticallyinterlinkedstructureofcategories,whichcanbemergedandcombinedtogenerateanexpressionforexistingornewconcepts(Svenonius,2000).CCisfaceted(Chatterjee,2016;Dawsonetal.,2006).UDCisanalytico-synthetic(Kaosar,2008)andfollowsthefacetedmethodofCCusingdifferentconnectingsymbolsinmixednotationsandusingsubjectfacetsincludingtimeandspace(Chatterjee,2016).IEEEandACMpossessfacetedstructures.DMOZhasonlyhierarchicalstructureandpredefinedcategories.BasedonthismetricwerankCCfirst,UDCsecond,ACMandIEEEthirdwhileDDCandLCCareenumerativestructures,andtherefore,cannotbeincludedinthelist.

Facetedsearchmeanstonavigateorbrowsethroughthefacetedstructureofafacetedclassificationscheme.Facetedsearchisalsoappliedbyselectingdifferentrangesandchoicesfromdifferentfacetsthataregivenbyanyfacetedsystemtosearchtherequiredcontents.ItisdifferentfromsearchcomplexityinthesensethatitlooksatthepatternandcriteriaofsearchthatexistinanyclassificationschemeeitherinthereOPACsorwebapplications.ThetheoryandphilosophyofCCsupportsfacetedsearch&browsingeconomically(Kong,2016),however,tothebestofourknowledge,noreal-worldapplicationdemonstratesitsusefulness.UDCisbasedonthefacetedapproach,whichsupportsfacetedsearch(Tunkelang,2009).LCCsupportsfacetedsearchwiththehelpofLCSH(McGrath,2007).LCCalsoprovidesfacetedsearchthroughtheFacetedApplicationofSubjectTerminology(FAST)application("FacetedApplicationofSubjectTerminology,"2017).DDCprovidesthefacetedsearchthroughtheOCLCClassify16application.Usingthismetric,theseclassificationschemescanberankedasDDCatfirstpositionbecauseDDCisadoptingthefacetedapproachalongwithitsnativeenumerativenatureandstate-of-the-artwebbasedsearchapplicationsdevelopedbyOCLC.LCCisatsecondpositionbecauseofitswebbasedsearchapplicationsanditsadaptationofcomparativelyrestrictedfacetedapproach.IEEE,forprovidingextensivechoiceofsearchingpatterns,standsatthethirdposition.ACMhaspoly-hierarchicaland

16http://classify.oclc.org/classify2/


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multi-facetedclassificationstructurealongwithrobustsearchmechanism;therefore,itisonfourthpositioninthislist.ThereareverylimitedfacetedsearchapplicationsofUDCandthereforeitstandsatfifthposition.DMOZhashierarchicalstructureinwhichtherequiredelementcanbeaccessedthroughakeywordsearch.Therefore,itisnotprovidinganyfacetedsearch.CChasnosearchapplicationsthatcouldconfirmitssupportforthefacetedsearch.

LODdatasetsmeanstheavailabilityofdatasetsofagivenclassificationsystemonLODcloud.Amongourchoiceofwell-knownclassificationsystemsUDC,LCC,DDC,IEEE,ACMandDMOZhavedatasetsintheLODcloudwhereasCChasnosuchdatasets.ThedefinitionsofclassesandpropertiesaregatheredinLinkedDataVocabularies(LOV),whichareusedfordescribingdifferenttypesofobjectsusedinLODcloud.Thesedefinitionsofdifferentthingsprovidevocabulariesforlinkingthelinkeddata(Foundation,2017).CC,UDC,DDC,LCC,IEEEandDMOZhavenoLOV,whereasACMhasLOVvocabularies.

Themetric“platforms”intheevaluationframework,considerstheapplicabilityofagivenclassificationsysteminreal-worldwebapplicationsandotherdigitalenvironments.Inthisregard,UDCissupportedbyUDCconsortium,DDCbyOCLC,LCCbyLibraryofCongress,ACMbyACMdigitallibrary,IEEEbyIEEEXploredigitallibrary,andDMOZbyOpenDirectoryProject.Tothebestofourknowledge,CChasnotbeenusedbyanyoftheonlineapplications.

Table3.RankingandAverageRankingofClassificationSchemes

Thewarrantsofclassificationworkasauthoritativeactsforclassificationiststoperformthecognitivepracticefordesigningtheclassesandconceptsintheclassificationsystem,theirstructuralpropertiesandthenputtingsubjectsinthespecifiedclasses(Beghtol,1986).CCand

Ranking

StructuralCom

plexity

Notationalbrevity

PredefinedStructure

RulesCom

plexity

TheoreticalLaw

s

Mnemonics

Hospitality

SearchCom

plexity

Usability

Precisionand

Accuracy

Multilinguility

Interoperability

SemanticSearch

Biasness

Enum

erativeStructure

FacetedStructure

FacetedSearch

Consistency

LODdatasets

LOVSupport

AverageRanking

CC 1 2 7 1 5 2 6 2 2 4 2 1 7 5 4 4 1 4 1 1 3.1

UDC 2 3 4 4 3 6 5 3 3 3 5 3 3 3 2 3 2 5 2 1 3.25

DDC 4 5 3 3 4 7 4 4 5 2 4 3 6 4 1 1 6 3 2 1 3.6

LCC 3 4 2 2 2 5 3 4 4 2 3 3 2 1 1 1 5 3 2 1 2.65

ACM 6 7 6 5 1 4 2 4 5 2 1 3 5 2 3 2 3 2 2 1 3.3

IEEE 5 6 5 5 1 3 2 4 5 2 1 3 4 2 3 2 4 2 2 2 3.15

DMOZ 7 1 1 5 1 1 1 1 1 1 6 2 1 6 4 1 1 1 2 1 2.25


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UDCuseliterarywarrant;DDCandLCCuseliterary&scientificwarrants.ACMandIEEEusescientificresearchwarrant,whileDMOZexhibitsnowarrantofclassification.

Intheaboveparagraphs,wecomparedandevaluatedtheselectedclassificationsystemusingtheevaluationmetrics(showninTable2),anddiscussedhowthesesystemscanberankedbasedonagivenevaluationmetric.However,togiveaholisticviewofthiscomparisonandevaluation,weintroducearankingscoreorlevelsrangingfrom1(meaninglowranking,notapplicable,ornotavailable)to7(meaninghighranking)inhowaclassificationschemeisbestamongitscounterpartsinthelist.Itisalsothecasethatforagivenmetric,multiplesystemsmaybelongtothesamerankinglevel.Byassigningtheserankinglevels,Table3comparesthesesystemsbasedon20metricsbyexcludingplatformsandwarrantsofclassification.Table3alsoreportstheaveragerankingoftheseclassificationsystems,showingDDCattopwithaveragerankingof3.6,followedbyACM=3.3,andUDC=3.25.ItcanbeconcludedthatDDCandUDCareamongthebestclassificationschemesfordescribingprintedaswellasdigitalcollections,whereasACMisbestforclassifyingdigitalcollectionsbelongingtoComputerSciencedomain.However,ACMclassificationsystemcanbeextendedtoincludeotherdomainsaswell.Figure1illustratesgraphicallythecomparisonandevaluationofthesesystems.

Figure1.ComparisonandRankingofClassificationSystems

Table4presentsthestate-of-the-artbibliographicclassificationontologiesincludingBibliographicontology,LCContology,DDContology,UDContology,andDMOZontology.Someoftheseontologiesweredesignedspecificallyforcertaintargetedapplicationse.g.,ACMontologyforACMdigitallibrary,andLCContologyforLibraryofCongressetc.,whereasothershavemultipleusagescenariosandhavebeenusedbyseveralapplications.Anexampleofsuchgeneral-purpose


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bibliographicclassificationontologyistheBibliographicOntology17,whichisusedbyseveralbibliographicservicesanddigitallibrariese.g.,digitalobjectidentifier(DOI),Zotero,andLibraryofCongressClassificationNumber(LCCN)permalinkservice(Giasson,2012).Thisevaluationframeworkcomparestheseontologiesbasedontheirsize(intermsofnumberofclasses),usageinthestate-of-the-artapplications,LODsupport,theavailabilityofdatasetsondatahub18,andLOVsupport.BylookingatTable3,ACMshowmorecomprehensivenessintermsofnumberofclasses,triplesandLOVsupport.

ClassificationandCategorizationOntologies

No.ofclasses

Applications LODdatasets

LODdatasetstriples

LOVsupport

Bibliographicontology19 69 LibraryofCongressandBibBase

ü 200000 ü

LCContology20 40+ LibraryofCongress

ü NotGiven û

DDContology21 20+ OCLC ü 402288 û

UDContology22 2,600 UDC23 ü 69,000 û

ACMontology24 1469 ACM ü 12402336 ü

IEEELOMmetadataontology(Casali,Deco,Romano,&Tomé,2013)

9 IEEE25Xploredigitallibrary

ü 91564 ü

DMOZontology26 Notgiven

OpenDirectoryProject

ü Notgiven û

Table4.Comparisonofclassificationandcategorizationontologies

18 https://datahub.io19http://purl.org/ontology/bibo20http://id.loc.gov/21http://dewey.info/22http://udcdata.info/23http://udcdata.info/24http://dl.acm.org/ccs/ccs.cfm25http://ieee.rkbexplorer.com/id/26https://www.dmoz.org/rdf.html


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Issues&ChallengesinClassificationResearch

Althoughbibliographicclassificationhasbeenpracticedsincetheuseofbooksandtheinceptionoflibrary&informationsciencepractices,furtherresearch&developmenteffortsarerequiredfortomeet theclassificationneedsof thedigitalage.Especially,with thearrivalofdigitalholdings,researchersfaceseveralissuesandchallenges.Forexample,automatictextclassificationperformscategorizationofresourcesusingordinarymetrics includingTF-IDFandclassification in its truesense isyet tobeachieved (Yi,2006).Tohandle the issue, text classification isalsocarriedoutthrough semantic indexing but its accuracy and precision are yet to be achieved. Semantic andstructuralrelationshipsamongdifferentpartsoftextcorpusisstillatinfantlevelandhasnotbeenexploitedtotheirfullestsothatthesecanbeusedintextclassificationinmoremeaningfulways.Other challenges in text classification include handling huge data resulted by applying aclassificationscheme,dynamisminclassification,andstructuredissimilarityamongclassificationschemesalthoughtheyagreeuponsubjectastheprimarycharacteristic.

The biasness in DDC and LCC needs to be resolved. Several revisions and proposals are putforwardforaddressingtheproblemofsystematicknowledgeorganizationandsearchingthroughnaturallanguageterms(Miksa,2007).Therearevariousissuesregardingthestructuralupdates,search&retrievalcriteria,andvisualization(Slavic-Overfield,2005).

TherearetwomainchallengesfortheapplicationofthebibliographicclassificationprinciplesinclassifyingtheWeb.First,theprinciplesofthebibliographicclassificationareformulatedfortheprinteddocuments,whichshouldalsobeapplicabletodigitalcollections.Foraddressingthesechallenges,thereisneedtoapplyandmodifybibliographicclassificationprinciplesindigitalenvironments.Second,itisrequiredtoexploithiddenhierarchiesandconceptstobebetterclassifiedbytheprinciplesofbibliographicclassificationforprecisediscovery,searchandretrieval(J.Mai,2004).

Theissueofdependentprocessofclassificationofanyobjectperpredefinedcriteriaandprinciplesisimportanttoaddressforfindingaplaceinthisageofsearchengines.Thisissuecanbetailoredbytheprinciplesofclassification,sothattheconventionalprinciplesaremodifiedtoconsiderthepurposeofclassificationanddomainofobjects.Forthisissuesemanticwebandontologiescanplayavitalroleinbibliographicclassification,whichcanprovideindependentclassificationofthebibliographicclassificationpredefinedtheories(Hjørland,2012).

Theissueofheterogeneityconflicts,whicharisebecauseoftheinconsistenciesandstructuraldivergences,arethechallengesforthesemanticinteroperability.Semanticinteroperabilitycanbebroughtintothebibliographicrecordsinsidethebibliographicsystemandacrossthesystemsthroughdifferentphasesofinterlinking,evaluation,analysis,remodeling&conversionforanalyzing,andrestructuringthebibliographicdata(Tallerås,2013).

Bibliographicdataisinmulti-format,multi-topical,multi-lingualandmulti-targeted.Fortacklingtheseissues,thebibliographicdatamustbemademutuallyinteroperableformakingit


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interlinked,searchable,andpresentedinaharmonizedwayacrosstheboundariesofthedatasetsanddatasilos.Theinteroperabilityproblemarisesatthesyntacticlevelformakingconsistentthecharactersets,notations,dataformatsandrecordsindifferentsystems.Theinteroperabilityproblemisalsoarisingatthesemanticlevelbecauseofthedifferenceindatainterpretationsanddifferenceinvocabularies,andprecisionlevelsindataencoding.Publishing,collectingandmaintenanceofbibliographicdatabymultiorganizationsthroughownestablishedstandardsandbestpracticesinWeb2.0(Hyvönen,2012).Withtheseproblemsinhand,thetransitionofthisdatafromsyntacticalWebtoSemanticWebisachallengeforbringingtheuniformityinrecordsthataregeneratedbydiversesources,encodedinmulti-bibliographicsystems,crossbibliographicsystemsinteroperability,thevisualizationofbibliographicdataaccordinglyasperneedfordifferentcontexts.Foraddressingtheseproblemsthereisaneedforcoordinationandcollaborationbetweenbibliographicdatapublishersandthetechnicaldevelopersofthewebapplications(Hyvönen,2012).

Thereisvarietyofmetadatastandardsandschemasfordefining,managing,resourcediscovery,search&retrieval,preserving,mapping,cross-walking,integrity,accuracy,andauthenticityofmetadataandbibliographicdata.Butforthesetaskstobehandledwithgreatsimplicity,semanticrichnessandaccuracy,auniversalallinonemetadataformatandschemaistheneedoftheday(Ramesh,Vivekavardhan,&Bharathi,2015)togetoutofthisjungleofstandards(Gartner,2016).Thisway,themetadatapublishersandmanagerscouldgetrelievedandthejobwillbecomeeconomicintermsoftime,management,andsearch&retrieval.

ThreemaintasksweresetinSemanticPublishingchallenges2015.Thesetasksare:(i)extractingdataonworkshops’qualityindicators;(ii)extractingdataonaffiliations,citations,funding;and(iii)interlinking.Severalchallengeswerefacedwhilefulfillingthesetasks.Thesetasksarebeingfulfilledthroughaproposedsolution,whichiscomposedofatextminingpipeline,LODeXporterandNamedEntityRecognition(NER)fornamedentitiesextractionformtextandlinkingthemtoresourcesontheLODcloud(Sateli&Witte,2015).

In(Peroni,2012)threemainissuesofsemanticpublishingareaddressedwhichare:lackofdocumentpublishinguniversalmetadataschemasaccordingtopublishingvocabulary,lackingofefficientuserinterfacethatarebasedonmodelsandtheoriesofsemanticpublishing,andthereisaneedforatoolthatsemanticallylinkanddescribedocumenttext.Theseissuesrequiretheurgentneedforcomprehensiveontologiesfordocumentpublishingdomain.

(Ferrara & Salini, 2012) tossed 10 challenges for multiple dimensions of data in terms ofbibliographic analysis. These challenges are: (i) analyzing bibliographic data in amultidimensionalpattern;(ii)discoveringandintegratingdatacomingfromdiversesources;(iii)detecting multiple references to the same item and cleaning, normalizing, and disambiguatingbibliographicdatarecords;(iv)analyzingmultidimensionalnatureofbibliographicdatathroughmultivariateanalysis foraggregatingthedata;(v)comparingdifferentelementsofbibliographicdata and its ranking accordingly, (vi) aggregating indexes of different nature with respect to


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differentparameters,dimensions,andelementsofbibliographicdata;(vii)dealingwithmultipleindexes for thesame itemwith differentvaluescoming fromdifferentsources; (viii)extractingandindexingtextualinformationfromtextcorpusinsupportoftextmining;(ix)analyzingtextualdata topic-wise and describing these topics for research and learning process and tracingdifferent trends; and (x) combining multidimensional information for finding trends inbibliographicdatacollection.

BibliographicclassificationsystemsarebeingincorporatedinLOD.InDewey.info27,aprototypeversionofDDCisdesignedforlinkingitsdatasetinlinkeddatacloud.TheintentionistoprovideaplatformforDDCdataontheWebhavingsummariesoftop3levelsofclassificationorderofDDC22ndeditionin11differentlanguageencodedinRDF/SKOS,havingactionableURIsforeveryclass,representationformachinesisinRDF,andforhumansinXHTML+RDFs,andserializationavailableinformatsofRDF/XML,TurtleandJSON,andwithSPARQLendpoint.(OCLC2011;MitchellandPanzer2013).However,thisversionofDDConLODcloudisstillatinfantstagetocoverdifferentsubjectsandtobewidelyusedingeneratingandcreatingdocumentsmetadata.

LibraryofCongressLinkedDataserviceprovidesaccesstocommonlyusedstandardsandvocabulariesdevelopedbyLibraryofCongress.Thisincludesdatavalues,controlledvocabularies,andpreservationvocabularieswhicharepartofthisservice.ThisserviceprovidesaccesstoLCSH,LCCnameauthorityfiles,LCC28,LCchildren'ssubjectheadings,LCgenre/formterms,thesaurusforgraphicmaterials,MARCrelators,MARCcountries,MARCgeographicareas,MARClanguages,ISO639-1languages,ISO639-2languages,ISO639-5languages,extendeddate/timeformat,preservationevents,andpreservationlevelroleandcryptographichashfunctions.TheauthoritiesandvocabulariescurrentlyincludedinthisservicearelistedontheLinkedDataservice(LibraryofCongress2014).However,itlacksinvocabulariesforsupportingPREMIS,MARC,MODS,METS,andMIX.

As presented in Section 2, several ontologies have been developed for describing and sharingknowledge about bibliographic classification. However, the available ontologies are limited inseveralwayse.g.,theseontologiesarenotthecompleteclonesofclassificationschemesofwhichthey are deemed to be ontologies and they also not mature enough in terms of metadatacollection. In addition, these ontologies still couldn’t break the cross-classification schememetadatacollectionbarriersi.e.,theyarenotinteroperableenoughtoharvestthemetadataacrossbibliographic ontology system. Therefore, further initiatives are required to develop maturedbibliographicontologieswhichfullyclonebibliographicschemesthatareinpracticaluseandhavestrong theoretical ground. These ontologies must be interoperable and sharing metadatacollectionwithotherbibliographicontologies. Inthisway in futurewecanhaveontology-basedgeneral bibliographic classification system by fusion of the new and existing bibliographicontologiesforbettermanagementoftheknowledgeartifacts. 27https://datahub.io/dataset/dewey_decimal_classification


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CONCLUSIONS

Withthearrivalofdigitalcollections,newchallengesofpreservation,curationaswellasresourcediscovery&access(retrieval)haveemergedthatneedsproperattention,whereclassificationschemesandontologiescanplayasignificantrole.BycomparingandevaluatingtheavailablebibliographicclassificationandcategorizationsystemsitisconcludedthatcurrentlyDDCisthebestclassificationsystemfollowedbyUDC,andACM.Thebibliographicclassificationontologiesarelimitedinonewayortheothere.g.,someofthesearecomprehensivelikeUDCandACMbutlacksupportforLODandLOVetc.,whileotherssupporttheselateraspectsbutlackcomprehensiveness.Keepinginviewtheavailablebibliographicclassificationontologiesandtheirlimitations,werecommendthatauniversalbibliographicclassificationontologyshouldbedevelopedbyusingtheclassesfromtheavailableontologiesandprovidingsupportintermsofavailabilityofdatasets,supportforinteroperability,LOD,andLinkeddatavocabularies.

Fordevelopingamoremeaningfulclassificationsystem,equallyapplicabletodigitalenvironments,itisnecessarytoconsiderthebookstructuralsemanticssuchastableofcontents,headings,chapters,sections,subsections,figures,algorithms,mathematicalequations,quotationsetc.,andthelogicalconnectionsincontents(Khusro&Ullah,2016;I.Ullah&Khusro,2016)aswellasaboutthebookinformationi.e.,thebibliographicdetailsoftheholdings.Tomeet,theformerrequirement,acomprehensiveontologylikeBookOnt(A.Ullah,Ullah,Khusro,&Ali,2016)couldbeused,whichcanbemappedwithanybibliographicontologylikee.g.,BibliographicOntology29.However,astheevaluationframeworkssuggest,DDC,UDC,andACMClassificationSystemshouldbeexploitedindesigningsuchageneral-purposeclassificationsystem.

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