Ontology-based
similarity
for
product
information
retrieval
Suriati
Akmal
a,1,
Li-Hsing
Shih
b,
Rafael
Batres
a,*
aDepartmentofMechanicalEngineering,ToyohashiUniversityofTechnology,Hibarigaoka1-1,Tempaku-cho,Toyohashi441-8580Japan bDepartmentofResourcesEngineering,NationalChengKungUniversity,No.1,UniversityRoad,Tainan701,Taiwan,ROC
1. Introduction
Duetothecomplexity ofproducts and drastictechnological changes,productdevelopment is becomingincreasingly knowl-edge intensive. Design is also multi-disciplinary in nature requiringavarietyofproductlife-cycleknowledge[1].Specifically, designteamsfaceaconsiderablechallengeinmakingeffectiveuse ofincreasingamountsofinformationthatoftenaccumulateand remaininindividualinformationsystems.Also,itisoftenthecase that product designers can reuse past designs rather than designingfromscratch[2].
Information retrieval consists of translating and matching a queryagainstasetofinformationobjects.Translationofthequery is necessary for converting the user requirements into the language provided by the information retrieval system. The informationretrievalsystemrespondstothequeryusingagiven algorithm and a similarity measure. Particularly, information retrievalplaysanimportantroleinareassuchasproductfamily design[3],productembodiment,anddetaileddesign[4].Shahet al.[5]presentacombinationframeworkthatconsistsofsoftware engineering, data engineering and knowledge engineering and designtheory.
In ordertosupportproduct informationretrieval and reuse, someauthorssuggest theuseofcase-basedreasoning(CBR) in whichdesignproblemsaresolvedbyusingoradaptingprevious designsolutions[4,6].
ACBRsystemiscomposedofdomainknowledge,acasebase, andasearchmechanismbasedonasimilaritymeasure.Domain knowledgereferstoknowledgeaboutthefeaturesofthedifferent objectsorentitiesthatacaseisabout.Acasebasecontainsasetof cases,each ofwhich describes aproblem and asolutiontothe problem. The problem is typically defined in terms of specific features of objects.Finally, a similarity measure quantifies the differences that exist between objects [7]. CBR uses similarity measurestoidentifycaseswhicharemorerelevanttotheproblem tobesolved.
Mostsimilaritymeasuresevaluatedifferencesbetweenvalues ofnumericpropertiessuchasinthenumericaldifferencebetween two given diameter values. However, many applications also requirenon-numericsimilarities.Forexample,case-based reason-ing systems for the conceptual design of products must be developedtoworkwithalimitedknowledgeabouttheproduct.
Nearly allof non-numeric similarity measuresare basedon syntacticgrounds.Forexample,theLevenshteindistance[8,9]can beusedtocalculatethesimilaritybetweentwowords,intermsof theminimumnumberofoperationsthatareneededtotransform oneofthewordsintotheother.However,fromthepointofviewof themeaningofthewordsthatarecompared,existingsyntactic similarity-measuresoftenresultinincorrectmatches.
ARTICLE INFO
Articlehistory:
Received4September2012 Receivedinrevisedform10June2013 Accepted31July2013
Availableonline21September2013
Keywords:
Semanticsimilarity Ontology
Productinformationretrieval Formalconceptanalysis
ABSTRACT
Productdevelopmentoftodayisbecomingincreasinglyknowledgeintensive.Specifically,designteams faceconsiderablechallengesinmakingeffectiveuseofincreasingamountsofinformation.Inorderto supportproductinformationretrievalandreuse,oneapproachistousecase-basedreasoning(CBR)in whichproblemsaresolved‘‘byusingoradaptingsolutionstooldproblems.’’InCBR,acaseincludesboth arepresentationoftheproblemandasolutiontothatproblem.Case-basedreasoningusessimilarity measurestoidentifycaseswhicharemorerelevanttotheproblemtobesolved.However,most non-numericsimilaritymeasuresarebasedonsyntacticgrounds,whichoftenfailtoproducegoodmatches whenconfrontedwiththemeaningassociatedtothewordstheycompare.Toovercomethislimitation, ontologiescanbeusedtoproducesimilaritymeasuresthatarebasedonsemantics.Thispaperpresents anontology-basedapproachthatcandeterminethesimilaritybetweentwoclassesusingfeature-based similaritymeasuresthatreplacefeatureswithattributes.Theproposedapproachisevaluatedagainst otherexistingsimilarities.Finally,theeffectivenessoftheproposedapproachisillustratedwithacase studyonproduct–service–systemdesignproblems.
ß2013ElsevierB.V.Allrightsreserved.
* Correspondingauthor.Tel.:+81532446716;fax:+81532446716.
E-mailaddress:rafaelbatres@hotmail.com(R.Batres).
1OnleavefromtheUniversitiTeknikalMalaysiaMelaka,76109DurianTunggal,
Melaka,Malaysia.
ContentslistsavailableatScienceDirect
Computers
in
Industry
j ou rna l h ome p a ge : w ww . e l se v i e r. co m/ l oc a te / c om pi nd
Semanticsimilaritymeasurescanbeusedinordertoovercome thelimitationsofsyntacticapproaches.Asemanticsimilarityisa functionthat assignsanumericvaluetothesimilaritybetween twoclassesofobjectsbasedonthemeaningassociatedtoeachof theobjects[10].Forareviewofsemanticsimilaritymetrics,the readerisreferredtothepaperofCrossandHu[11].
Recently, the useof ontologies for evaluating similarity has been reported in the literature [12,13]. Ontologies are formal modelsthatusemathematicallogictodisambiguateand define classesofthings[14].Specifically,ontologiesdescribeasharedand commonunderstandingofadomainintermsofclasses,possible relationsbetweenthings,andaxiomsthatconstrainthemeaning ofclassesandrelations[15].Aclassrepresentsasetofthingsthat share the same attributes. A relation is used to represent a relationshipamongtwoormorethings.Examplesofrelationsare lessthan,connectedto,andpartof.Classtaxonomiesaredefined bymeansofthesubclassrelation.Aclassisasubclassofanother classifeverymemberofthesubclassisalsoamemberofthesuper class.Axiomsaretypicallyrepresentedaslogicconstructionsthat formallydefineagivenclassorrelation.
Combined withautomatedreasoningapplications,ontologies canbeusedforseveralpurposessuchasknowledgeextractionand information retrieval. Unfortunately, ontologies are typically createdinanad-hocmanner,whichmayinfluencetheaccuracy ofthesimilaritycalculations.
Formalconceptanalysis(FCA)isadataprocessingmethodthat canbeusedtodesignontologies[16,17].FCAisbasedonasetof objectsandasetofattributes.Inthispaper,weuseFCAalongwith a theoretical framework for developing product and process ontologies.
Mostsemanticsimilaritiesaredefinedintermsofthenumber ofedgesbetween theclassesthat theycompare(edge-counting similaritymeasures). Othersemanticsimilarities aredefined in terms of features but use synsets for the comparison between wordsratherthanclasses.
Theunderlyingthesisinthispaperisthatifaclassrepresentsa setofthingsthatsharethesameattributes(suchasaclassinan ontology),wecanstatethataclassisequivalenttoanotherclassif
bothclasseshaveexactlythesameattributes.Thisimpliesthatthe morecommonattributesthataresharedbytwoclassesthemore similartheyare.Inthispaper,weshowhowanontology-based approachcandeterminethesimilaritybetweentwoclassesusing feature-based similarity measures that replace features with attributes.
The paper is organized as follows. Section 2 describes the theoretical framework for product representation used in this paper.Section3 providesdetails on theontologydevelopment. Section 4 describes the proposed ontology-based similarity measures.Sections5and6describetheevaluationofthesemantic measuresproposedinthispaper.InSection7,theeffectivenessof theproposedapproachisillustratedwithacasestudyon product-service-systemdesignproblems.Section8discussessomerelated workandSection9presentsconclusionsandsuggestions.
2. Theoreticalframeworkforproductrepresentation
Theoreticalframeworksforproductrepresentationrefertothe worldviewwithwhichproductinformationmodelsorontologies canbedevelopedinordertorepresentaproduct.Inthispaper,the theoreticalframeworkforrepresentingaproductisbasedonthe ISO15926standardwhichspecifiesanupperontologyfor long-termdataintegration,accessandexchange[18].Itwasdeveloped in ISO TC184/SC4-Industrial Data by the EPISTLE consortium (1993–2003) and designed to support the evolution of data throughtime.Theupperontologywasdevelopedasaconceptual data model for the representation of technical information of processplantsincludingoilandgasproductionfacilitiesbutitwas designedtobegenericenoughforanyengineeringdomain[19].
Inthistheoreticalframework,thedeviceisrepresentedinterms ofitsphysicalaspectsaswellasintermsofitsrelationtosome process(activityinISO15926).Theseaspectsareillustratedinthe modelsofFigs.1and2.
Adeviceisrepresentedasaphysicalobjectthatisdefinedin termsofadistributionofmatter,energy,orboth.Thedeviceisalso described in terms of its parts. This is possible through a mereologicalrelation thatrefers totherelationshipthat a part
Fig.1.Compositionofdevice.
hasinregardstothewholeofanobject.Mereologicalrelationsare reflexive,antisymmetric,andtransitive.
Physicalobjectsexistinreferencetoaspecificplace.Thelocation relation(relative locationinISO15926)isakind ofmereological relationthatisusedtolocateobjectsinaparticularplace.
Astreamisanotherkindofphysicalobjectthatisappliedto materialorenergymovingalongapath,wherethepathisthebasis of identity and may be constrained. For example, thematerial movingalongapipeisaninstanceofstream.
Thefunctionofaproductcanbedefinedasanintendedprocess associatedtothedevice.Forexample,thefunctionassociatedtoa sofaisrepresentedastheprocessofseatinginwhichthesofais involvedalongwithapersonthatsitsonit.Similarly,thefunction ofanelectricfanistogeneratecoolair.Inthiscase,thedescription ofthedeviceincludesinformationaboutthecoolingprocess.The coolingprocessis inturncomposed ofotherprocesses suchas conversion of electricity into rotary movement, convection, diffusion and heat transfer. Therefore information about the processorprocessesassociatedtothedeviceisanindispensable elementtocompletethedescriptionoftheproduct.
Different objects can participate in a process. Participating physicalobjectsincludethoseobjectsthataretransformedbythe process, those objectsthat are produced by theprocess, those objectsthatarenotaffectedbytheprocess(thedeviceitself,other tools,orinstruments), aswellasagents (suchas aperson ora controlsystem)thatparticipateorexecutetheprocess.
Asinthedescriptionofthedevice,aprocessisalsodescribedin termsofitsrelativelocationanditsmereology.
3. Ontologyconstruction
There area number of methodologiestodevelop ontologies includingUscholdand King’smethod[25],Gru¨ningerand Fox’s method[26],Noy andMcGuiness’s method[27],the METHON-TOLOGYframework[28],theCycmethodology,KACTUS,SENSUS, andtheOn-To-KnowledgeMethodology[29].
AccordingtoStevenset al.[30],thegeneralstagesforontology developmentare:identificationofpurposeandscope,knowledge acquisition,conceptualization,integration,encoding, documenta-tion,andevaluation.Wefollowthesegeneralsteps,butweuseFCA (see Appendix A) and the theoretical framework described in Section2toguidetheknowledgeacquisitionand conceptualiza-tionstages.
Candidate classes that may or may not appear in the final ontologyareidentified,andtheobjectcolumnofaFCAcontext tableispopulatedwiththeseclasses.
Subsequently, information sources such as scientific papers, technicalreports,andInternetresourcesareconsultedtodefine eachclassinnaturallanguage.Whenseveraldefinitionsarefound preferenceisgiventothosethatexplicitlydescribeparticipating objects, objects transformed by the process (inputs), objects produced by the process (outputs) and/or subactivities. When contradictions among several definitions of a given class occur expertscanbeconsultedtodisambiguate.
Formal attributes are identified from the natural-language definitionsusingthefollowingguideline.
Adevice(aphysicalobject)ischaracterizedby
1.Theprocessinwhichthedeviceparticipates. 2.Thecompositionofthedevice(itsparts).
In turn,a given process or theprocess in which the device participatesischaracterizedbythefollowing:
Theobjectthatistransformedbytheprocess(theinputofthe process).
Theobjectthatis producedbytheprocess(theoutputofthe process).
Thetoolthatispresentinanyinstanceoftheprocess. Thecompositionoftheprocess.
After adding the formal attributes, the context table is completedandalatticeisgenerated.Latticesinthispaperwere generated by means of the Grail algorithm [24], which is implementedinthesoftwareConceptExplorer.Finally,thelattice isusedtocreatetheontology.Thenamingofeachclassisdone basedonobjectorattributeslabelsfromthenodesinthelattice. Integrationis carriedout bymeansofaligning theresulting ontologywithanupperontologythatdefinesdomain-independent classes such as physical objects, activities, mereological and topologicalrelations.Forexample,aclassrefrigeratorisdefined asasubclassofphysical_object,whichisaclassoftheISO15926 upperontology.
4. Ontology-basedsemanticsimilarities
Inagivenontology,aclassisequivalenttoanotherclassifboth classes have exactly the same attributes2. Therefore, the more
commonattributesthataresharedbytwoclassesthemoresimilar theyare.
The proposed approach consistsof combining feature-based similarities with an ontology obtained with the procedure describedinSection3andusingformalattributesfromtheFCA as features. The feature-based similarities investigated in this paperare:theTverskyindex[21],theDice’scoefficient[22],the Jaccard’s coefficient [31], the Overlap coefficient [23], the all-confidencesimilarity[23],andtheCosinesimilarity.
Forexample,Tverskyindexbecomes
simTverskyðC1;C2Þ¼
where
a
andb
areparameterscalculatedaccordingtoRodriguez andEgenhoffer[32],A1andA2arethesetsofattributesofclasses C1andC2,jA1\A2jisthetotalnumberofformalattributesshared by C1 and C2, jA1j and jA2j represent the number of formal attributesofC1andC2.WealsousethesimilarityequationsgivenbyvanderWekenet al.[33]butusingformalattributesetsinsteadoffuzzysets:
simvanDerWeken1ðC1;C2Þ¼
Norestrictionexistsforoneoftheclassestobeasubclassora superclassoftheother.Inotherwords,theclassestobecompared canhappenanywhereintheontology.
Inaddition,wealsoinvestigateacompositesimilarityobtained bycombiningsemanticsimilarities:
simCompositeðC1;C2Þ¼w1sim1þw2sim2 (4)
wherew1 andw2 areweightsand sim1 andsim2 representtwo differentsemanticsimilaritymeasures.
Inordertocompareagainstedge-countingsimilaritymeasures, calculationsarealsocarriedoutusingbothWu-Palmer’s[20]and Lin’s[10]similaritymeasures.
2Theattributesofaclassalsoincludethoseattributesinheritedfromitsparent
5. Experimentalevaluationoftheproposedapproachusing humanjudgment
This experiment focuses on the domain of electric home appliancesfortheevaluationofdifferentsemanticsimilarities.The evaluationis carriedoutbymeasuringthedegreeofcorrelation betweenthecalculatedsimilarityscoresand scoresobtainedby human judgments. For this purpose, a questionnaire was administeredto 30 respondents. The questionnaire asked each respondenttorankthelikenessbetween‘electrickettle’andeach of 17 home electric appliances. Respondents then rated the similarity of the pairs on a 1–17 scale, with lower numbers indicatinghighersimilarity.
Thecomparisonwascarriedoutbycalculatingthecorrelation coefficientandthesumofsquarederrors.
Thelevelofinconsistencyofeachquestionnairewascalculated withthefollowingformula.
whereqijisthevalueofthescorethatparticipantisubmittedfor
pairjand
m
ijisthemeanofthescoresofalltheusersexceptthatof useriforpairj.Usingthisformula,questionnaireswithvaluesofdi abovetwostandarddeviationsfromthemeandi¯ wereexcluded fromtheanalysis.Theaveragestandard deviationsofthescoresacross respon-dentswerealsoevaluatedtoidentifyinconsistencies.Sinceoneof thequestionnaireshadastandarddeviationlowerthanaverage,it wasnottakenintoaccount.Withthislastchange,thesamplesize wasreducedfrom30to27.
Finally, individual pair scores with one standard deviation beloworabovethepairmean¯qjwereeliminated,whichaccounted for4%ofthetotaldata.
Subsequently, theaveragescoreswerenormalizedusing the followingtransformation:
sj¼ ¯qjq
min
qmaxqmin (6)
wheresjrepresentsthesimilarityofpairj,qmax¼17andqmin¼1. ValuesofsjareshowninthefirstcolumnofTable 1.
5.1. Ontologydevelopment
Inthissection,wedescribethedevelopmentofanelectrichome applianceontology, whichis basedon themethoddescribedin Section3.Thelistofpotentialclasseswasextractedfromproduct categories in Amazon.com and the attribute information was obtainedusingthecharacterizationexplainedinSection3,using expertconsultationsandbrainstorming.Inthedevelopmentofthe ontology, wefocused onthe processorprocesses inwhich the given appliance participates or is involved. Therefore, formal attributesincludeareferencetotheprocessoradescriptionofthe processintermsoftheobjectsthataretransformedbytheprocess andtheobjectsthatareproducedbytheprocess.Forexample,the formalattribute identificationofanelectrickettlestarts bythe analyzingitsmainprocessassociatedtoit,whichisaprocessthat produceshotwater.Heatingisapartofthatprocess.Inorderto producehotwater,theelectrickettleconsumeselectricitythatis converted into thermal energy that is used to heat water. Therefore, the formal attributes of an electric kettle become ‘heats’, ‘produces hot water’, ‘heats water’, and ‘consumes electricity’.
Withformal-attributeinformationobtainedthisway,acontext tablewascreated (Fig. 3). Subsequently,Concept Explorer[24] wasusedtogeneratetheconceptlatticeshownin Fig. 4.After analyzingandcorrectingthelattice,thefinallatticeand formal-attributeinformationwereusedtodevelopanontologyusingthe Prote´ge´ ontologyeditor[34].Subsequently,theresultingontology wassavedinOWLformat[35].
Strictlyspeaking,formalattributeinformationmustbeinthe formofaxiomsasinthefollowingexample.
Classfiltration: SubClassOf:
heating_device SubClassOf:
producessomehot_water
However, for simplicity in the similarity calculation, formal attributeswereaddedasOWLproperties.Forexample,theformal attributefor‘‘produceshotwater’’isdeclaredasfollows:
Declaration(ObjectProperty(:produces_hot_water))
Electricdishwasher 0.29 – 0.29 0.01 0.22 0.20 0.25 0.90 0.87 0.13 0.22 0.22
Washingmachine 0.32 – 0.29 0.01 0.22 0.20 0.25 0.90 0.87 0.13 0.22 0.22
Electricclothesdryer 0.62 0.40 0.57 0.18 0.50 0.50 0.50 0.93 0.93 0.33 0.50 0.50
Hairdryer 0.76 0.40 0.57 0.18 0.50 0.50 0.50 0.93 0.93 0.33 0.50 0.50
Waterheater 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Electricblanket 0.67 0.50 0.67 0.18 0.57 0.50 0.67 0.97 0.94 0.40 0.58 0.58
Toaster 0.73 0.33 0.50 0.18 0.40 0.33 0.50 0.93 0.87 0.25 0.41 0.41
Breadmachine 0.52 0.33 0.50 0.18 0.40 0.33 0.50 0.93 0.87 0.25 0.41 0.41
Electricoven 0.80 0.33 0.50 0.18 0.33 0.25 0.50 0.92 0.80 0.20 0.35 0.35
Microwaveoven 0.72 0.33 0.50 0.18 0.40 0.33 0.50 0.93 0.87 0.25 0.41 0.41
Vacuumcleaner 0.18 – 0.33 0.01 0.25 0.25 0.25 0.90 0.90 0.14 0.25 0.25
Televisionset 0.08 – 0.50 0.01 0.33 0.25 0.50 0.97 0.91 0.20 0.35 0.35
Roomelectricheater 0.81 0.50 0.67 0.20 0.57 0.50 0.67 0.97 0.94 0.40 0.58 0.58
Roomair-conditioner 0.49 0.40 0.57 0.18 0.40 0.33 0.50 0.93 0.87 0.25 0.41 0.41
Conventionalelectricfan 0.25 – 0.33 0.01 0.25 0.25 0.25 0.90 0.90 0.14 0.25 0.25
Refrigerator 0.34 – 0.33 0.01 0.22 0.20 0.25 0.90 0.87 0.13 0.22 0.22
Blender 0.21 – 0.33 0.01 0.25 0.25 0.25 0.90 0.90 0.14 0.25 0.25
Correlationwith humanjudgment
1.00 0.782 0.731 0.777 0.729 0.795 0.608 0.272 0.708 0.781 0.726
Fig.3.Contexttableforanontologyofhomeelectricappliances.
ThisresultedinanOWLfilewith33classes,39properties,and5 levelsintheclasshierarchy.
5.2. Similaritycalculation
AprogramwasdevelopedinJavausingtheontologylibrary Jena[36].Theprogramreadstheontologyandthenamesofthe two classes to be compared. Firstly, it extracts the formal attributeinformation of each class in the ontology.Then, the programproceedsto calculatethecardinalities for eachsetof attributes,theminimumandmaximumvalues,andthenumber of overlapped attributes. Attributes of a class include those inheritedfromallitsparent classes.Similaritycalculationsare thencarriedoutusingthefeature-basedsimilaritiesasexplained in Section 4. Then the Wu-Palmer’s and Lin’s similarities are calculatedbyedgecounting,usingthetaxonomystructureofthe ontology.
5.3. Experimentresults
Table 1summarizesthecalculationresultsoftheinvestigated similaritiesratingbetween17classcomparisons.
Weintroduced‘device’assubclassofphysical_object(definedin ISO 15926) and made ‘home electric appliance’ a subclass of ‘device’.FromTable 1,itcanbeseenthat thebestperforming similarity is the Overlap coefficient (simOverlap) with R=0.795
followedbytheWu-PalmersimilaritywithR=0.782,theCosine similarity (simCosine) with R=0.781, and Dice’s coefficient with
(simDice)withR=0.777.
Afterconsideringeverypossiblecombinationoffeature-based similaritiesforthecompositesimilarity equationof Eq.(4), the besttwocombinationswere:
simCosineþJaccardðC1;C2Þ¼1:887simcosine0:887simJaccard (7)
withacorrelationofR=0.817and
simDiceþJaccardðC1;C2Þ¼1:966simDice0:996simJaccard (8)
withacorrelationofR=0.816.
Theweightsw1andw2wereobtainedbynumericoptimization so as to minimize the residual sum of squares between the compositesimilarityandsjofEq.(6).
5.4. Analysisoftheresults
Toeliminatebiasesintheanalysisoftheresults,weremoved thosepairsthatproducedsquarederrorsgreaterthantwotimes
thestandarddeviation.Thepairs(electrickettle,televisionset)and (electrickettle,electricoven)producedthebiggestsquarederror. After removingboth pairs, thecorrelationvalueof theOverlap coefficientincreasedtoR=0.947.Again,simCosine (R=0.922)and
simDice (R=0.919) were second and third in performance,
respectively. For the combined similarities, simCosine+Jaccard
in-creasedtoR=0.950andsimDice+JaccardincreasedtoR=0.947.
A hierarchical cluster analysis was conducted in order to comparerelativelyhomogeneousgroupsofresults.Thecluster analysis was equally applied to both the human assessment results and the results obtainedwith the Overlap coefficient. Clustering was carried out using Ward’s minimum variance algorithm.
Acomparisonoftheclustersindicatesthatmostoftheobject pairsthatbelongtooneclusterwiththeOverlapcoefficientalso belongtoaclusterintheresultsofhumanjudgment.Asshownin Fig. 5, only (electric kettle, televisionset), (electric kettle,air conditioner), and(electrickettle,breadmachine)weregrouped intoanothercluster.Thisisprobablyduetomissingattributesin theFCAcontexttable.Alternatively,anotherpossiblereasonisthat thesethreepairs wereparticularlydifficulttojudgeduringthe answeringofthequestionnaire.
6. Experimentalevaluationoftheproposedapproachusing Websearch
This experiment uses the Web to evaluate the different similarity measures. In order to increase the accuracy of the Web-basedevaluation,amoretechnicaldomainwasselected.For this reason, we chose machining processes as the technical domain.Moreover,theevaluationwascarriedoutwiththesearch enginesprovidedbyGoogle’sScholarandElsevier’sScirus.
Despitetheexistenceofavastvarietyofmachiningprocesses, inordertoobtainacompactontology,thescopeofthisexperiment waslimitedtomechanicalmaterialremovingprocesses.Inorderto developtheontology,severalcommontextbooks[37–41]andan Internetsource[42]wereconsulted.Thepotentialclassesarelisted inthefirstcolumnofTable3.
Duringtheconstructionoftheontology,inordertodetermine the formal attributes for the context table, all the material removingprocesseswerecharacterizedaccordingtotheprocess characterizationexplainedinSection3.
Fig. 6 shows thecontexttable withtheclasses of material removingprocesses.Theresultingconceptlatticeispresentedin Fig.7.Thesimilaritymeasureswerethesameasintheexperiment ofSection5.TheabovementionedJavaprogramwasusedinallthe calculations.
Thistime,theresultingsimilarityscoreswerecomparedagainst theWeb-basedsimilaritydenotedbyEq. (9).
vðti;tjÞ¼1dðti;tjÞ (9)
where
v
(ti, tj) is the Web-similarityof terms ti, tj, and d(ti, tj)representsthedistancefunctionproposedbyCilibrasiandVitanyi [43]alsoknownasthenormalizedGoogledistanceorNGD.The distancefunctionofCilibrasiandVitanyiisdescribedbyEq. (10).
dðti;tjÞ¼
max logfðtiÞ;logf tj
logf ti;tj
logMmin logfðtiÞ;logf tj
(10)
wheref(ti),f(tj)andf(ti,tj)givethenumberofhitsforthetermsti,tj
and(ti,tj),respectively.Mcorrespondstothenumberofindexed
documentsinagivenWebsearchengine.Thenumberofindexed documentsofSciruswhichisreportedonitsWebpageis4.6108 asofMay21,2012.AvalueofM=5.8108forGoogleScholarwas obtainedfromanearlierestimate[44]andbyassumingagrowth rateof2.7%basedontheworld-wideaverageannualincreaseof academicpapers[45].
In ordertorestrict theWeb searchtothe domainof study, keywords in both search engines were formulated with the inclusionoftheterm‘‘machining’’andsearchwascarriedoutusing doublequotes.
Forexample,forthesimilaritybetweencounterboringandspot facing, search with Scholar for ‘‘machining’’ ‘‘counterboring’’
results in f(counterboring)=1019 hits; search for ‘‘machining’’ ‘‘spot facing’’ produces f(spot facing)=620hits; and search for ‘‘machining’’ ‘‘counterboring’’ ‘‘spotfacing’’results in f(counter-boring,spotfacing)=56hits.SubstitutingthesevaluesinEq.(10) gives d(counterboring, spotfacing)=0.2146. Using Eq. (9) we obtain
v
(counterboring,spotfacing)=0.7854.Calculationswerecarriedoutforpairwisesimilaritiesbetween allthepairsof processes,resultingin45 comparisons.Table 2 summarizestheresultsofthecalculations.
The best single similarity measure was the van der Weken similarity (simvanDerWeken2) withcorrelationcoefficients of R=0. 828,andR=0.916,forScirusandScholarrespectively.Then,the Jaccard’scoefficient(simJaccard)cameupsecond.
7. Casestudy
Inthiscasestudywefocusontheeffectivenessofthe ontology-based semantic similarities in the context of product-service system(PSS)design.APSSisamixofbothproductsandservices thatisoftenassociatedtobettersustainability.InthedesignofPSS systems,onecommondesigndecisionistheselectionofthetypeof servicethatcanbeintegratedwithagivenproduct[6].
A CBRsystemwasdevelopedin Javabyextending theopen sourcesoftwareFreeCBR.AsinanytraditionalCBRsystem,each caseisdefinedintermsofaproblemandasolution.Inthiscase study, the problem is defined in terms of case features that
representcharacteristicsof a givenproduct. Thecase features can be numeric or semantic. For numeric features, the index approachproposedbyLinet al.isused[6].Thesemanticfeature is specifiedas the classto whichthe productbelongs, that is definedinaproductontology.Thesimilarityforsuchsemantic featurewas calculated using the Overlapcoefficient and the formalattributesofeachclass.AscreendumpoftheCBRsystem isshowninFig. 8.
ThecasesimilarityintheCBRsystemiscalculatedusingthe equationproposedbyKolodnerandSimpson[46]:
Sðt;rÞ¼
whereSðt;rÞistheglobalsimilaritybetweenthetargetcasetanda
sourcecaser;wiistheweightoffeaturei; fitisthevalueoffeaturei
is calculated according to the following criteria, which is based on the Overlap coefficientand a similarity for numericalattributes.
i is thesetofformalattributesoftheclassspecifiedin feature ft
i;Ariisthesetofformalattributesoftheclassspecifiedin feature fr
i;and fimax and fimin arethemaximumandminimum numericvaluesoffeature fi,respectively.
7.1. Ontologydevelopment
A product ontology was developed based on the procedure describedinSection3andusingthelistofproductsreportedin[6]. The resulting concept lattice is shown in Fig. 9. The product
ontology extendsthe upperontology defined inthe ISO15926 standard[15].Tocarryoutthealignment,threeclasseswereadded as subclassesof physical object: substance,mixture, and device basedon[47].
DuringthepreparationoftheFCAcontexttable,attributeswere selectedbyinvestigating theprocessor processesinwhich the productparticipatesor isinvolved. Eachprocesswasdescribed accordingtotheprocesscharacterizationexplainedinSection3. Forexample,theobjectsthataretransformedduringtheoperation ofacopierarethedatainputbytheuser,electricity,andpaperand theobjectsthatareproducedbythesameprocessarethecopied printedpaper.Thus,theattributesofthecopierbecome:consumes data,consumeselectricity,consumespaper,andproducesprinted paper.
7.2. Experimentsetup
The case base was populated with information about 47 successfulproductservicessystems.Eachcasewasdescribedin terms of numerical and semantic features. Based on [6], the followingnumericfeaturesandweightswereused:placeofusage of the PSSsystem (w1¼0:116), frequency of usage of the PSS
temperaturerangeof theterritory(w12¼0:059).Theallowable
values for each numeric feature and their meaning is also explained in [6]. For example, the index used to describe the place of usage of the PSS system is defined for integer values rangingfrom1to3,where1representsindoor,3outdoorand2 both.Amongthesefeatures,productfashioncycle,volume,weight, usefullife, andpriceareproduct features.Theobjective ofthis experiment wastoevaluatethepossibility ofusing a semantic featureasareplacementofsomeoftheproductattributes.The
semanticfeatureconsistedoftheclassofproductdefinedinthe productontology.
Initially,two experimentswerecarriedout. Theobjectiveof experiment 1 was to provide a reference for comparing the proposedapproach.Forthispurpose,allthequeriesinexperiment 1consistedofvalues forallthenumericalfeatureswithoutthe semanticfeature.
In experiment 2, queries wereformulated by replacing two product features (product volume and product weight) by the
correspondingclassofproductfromtheontology.Theweightfor thissemanticfeaturewassettow13¼0:07.
Thecasesimilarityinbothexperimentswerecalculatedwith Eqs. (11)and(12).
The queries were formulated with the product information from each of the cases stored in the case base. Therefore, 47 problemsweredefinedwiththeproblemdataofthe47casesinthe casebase,resultinginatotalof94experiments.Theobjectivewas tofindtheservicestrategyandthencomparetheresultwiththe
Table2
Theresultsforexperimentsofmaterialremovalprocess.
Pairs Scirus Scholar Wu
Palmer
Lin Dice All Confidence
Overlap vander Weken1
vander Weken2
Jaccard Cosine Tversky
Counterboring with
Counterboring 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Milling 0.543 0.546 0.286 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Countersinking 0.862 0.857 0.727 0.443 0.800 0.800 0.800 0.800 0.800 0.667 0.800 0.800
Drilling 0.607 0.605 0.500 0.140 0.625 0.500 0.833 0.900 0.643 0.455 0.645 0.500
Spotfacing 0.785 0.797 0.833 0.620 0.900 0.900 0.900 0.900 0.900 0.818 0.900 0.900
Boring 0.514 0.687 0.800 0.443 0.842 0.800 0.889 0.900 0.818 0.727 0.843 0.800
Reaming 0.763 0.766 0.800 0.443 0.842 0.800 0.889 0.900 0.818 0.727 0.843 0.800
Turning 0.548 0.538 0.286 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Tapping 0.695 0.703 0.800 0.203 0.842 0.800 0.889 0.900 0.818 0.727 0.843 0.800
Millingwith Milling 1.000 0.999 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Countersinking 0.581 0.566 0.286 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Drilling 0.836 0.798 0.400 0.063 0.727 0.667 0.800 0.929 0.867 0.571 0.730 0.667
Spotfacing 0.515 0.526 0.250 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Boring 0.686 0.729 0.286 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Reaming 0.674 0.657 0.286 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Turning 0.883 0.805 0.500 0.063 0.800 0.800 0.800 0.933 0.933 0.667 0.800 0.800
Tapping 0.706 0.669 0.286 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Countersinking with
Countersinking 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Drilling 0.636 0.628 0.500 0.140 0.625 0.500 0.833 0.900 0.643 0.455 0.646 0.500
Spotfacing 0.771 0.798 0.727 0.443 0.800 0.800 0.800 0.800 0.800 0.667 0.800 0.800
Boring 0.509 0.674 0.600 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Reaming 0.789 0.775 0.600 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Turning 0.567 0.542 0.286 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Tapping 0.709 0.712 0.600 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Drillingwith Drilling 1.000 0.988 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Spotfacing 0.566 0.573 0.400 0.140 0.625 0.500 0.8333 0.900 0.643 0.455 0.645 0.500
Boring 0.697 0.773 0.500 0.140 0.667 0.556 0.833 0.909 0.714 0.500 0.680 0.556
Reaming 0.729 0.723 0.500 0.140 0.667 0.556 0.833 0.909 0.714 0.500 0.680 0.556
Turning 0.808 0.767 0.400 0.063 0.727 0.667 0.800 0.929 0.867 0.571 0.730 0.667
Tapping 0.762 0.736 0.500 0.140 0.667 0.556 0.833 0.909 0.714 0.500 0.680 0.556
Spotfacigwith Spotfacing 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Boring 0.474 0.546 0.545 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Reaming 0.694 0.635 0.545 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Turning 0.502 0.413 0.286 0.063 0.533 0.400 0.800 0.900 0.600 0.364 0.566 0.400
Tapping 0.638 0.581 0.545 0.203 0.737 0.700 0.778 0.800 0.727 0.583 0.738 0.700
Boringwith Boring 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Reaming 0.601 0.794 0.800 0.443 0.889 0.889 0.889 0.909 0.909 0.800 0.889 0.889
Turning 0.669 0.723 0.250 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Tapping 0.602 0.783 0.800 0.443 0.889 0.889 0.889 0.909 0.909 0.800 0.889 0.889
Reamingwith Reaming 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Turning 0.661 0.639 0.286 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Tapping 0.805 0.805 0.800 0.443 0.889 0.889 0.889 0.909 0.909 0.800 0.889 0.889
Turningwith Turning 1.000 0.998 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Tapping 0.707 0.667 0.286 0.063 0.571 0.444 0.800 0.909 0.667 0.400 0.596 0.444
Tappingwith tapping 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Correlation withScirus
1 0.983 0.711 0.786 0.764 0.743 0.747 0.628 0.828 0.787 0.770 0.743
Sumofsquared errors(Scirus)
2.409 9.249 0.588 1.04 1.269 2.133 0.499 1.352 0.562 1.040
2s(Scirus) 0.125 0.352 0.050 0.051 0.069 0.098 0.051 0.058 0.050 0.051
Correlation withScholar
0.983 1 0.835 0.865 0.877 0.858 0.822 0.614 0.916 0.891 0.882 0.858
Sumofsquared errors(Scholar)
2.058 9.259 0.291 0.727 1.019 1.923 0.256 1.124 0.272 0.727
already known service strategy of the corresponding case. For example,problem1describesacertainkindofwashingmachine thatwasusedinPSSthatprovidedarepairservice.Inthisexample, itisthusexpectedthatallifnotmostofthenbestmatchesreturn repairasthesolution.
Theexecutionofeachqueryresultedinarankedlistofmatches eachofwhichincludedproductinformation,theproposedservice strategy,andaglobalsimilarityvalue.Thentheresultingservice strategies werecompared againstthe originalservice. Table 3 showstheresultsforbothexperiments.Thebestfivematchesare shown for each problem. From the overall results, it can be observedthattherearenineproblems(Nos.1,5,10,14,18,26,28, 43and45)inwhichtheresultsofexperiment1areidenticalwith thoseofexperiment2.Forexample,thebestfiveservicestrategies in problem 1 were: refrigerator-repair, computer-repair, water heater-repair,laserprinter-repairandLCDmonitor-repair,allof whichareconsistenttotherepair servicecorresponding tothe solutionofproblem1.
Otherproblemsproducedslightlydifferentresults.Forexample, inexperiment2,problems11,12,19,20,33,34,37,38,40,41and42 produced the same five best matches found in the results of experiment1butwithadifferentranking.For example,inproblem 11bothexperimentsresultedintreadmill-lease,dryer-lease,LCD
TV-lease,refrigerator-leaseanddishwasher-lease.However,while treadmill-lease hasthe highestrankinexperiment 1,itappears secondinexperiment2.
Inaddition,therewere27results(suchasproblems2–9)that differedinoneortwocases.Forexample,theresultsforproblem2 includeanInternet-baseddigitalcalendarwhichisfalsepositive. On the other hand, some results of experiment 2 were good matchesalbeitbeingmissinginexperiment1.Forexample, (sofa-lease and platform bed-lease instead of jewelry-rental and handbagrental)inproblem9aregoodmatches.
Furthermore, the results of experiment 2 for problems 30 (photocopy-service), 31 (scanning-service) and 32 (laminating-service)arebetterwhencomparedtotheresultsofexperiment1in whichnotonlythebest5matchesrefertoaservicethatequalsthat ofthecasefromwhichthequerywasformulated(payperservice unit)but also theproductis morecompatible withthatof the suggested service. For example, experiment 2 for problem 30 resulted in laundry-service, printing-service, eyeglass cleaning-service,scanning-serviceandfax-service.Amongthese,printing, scanningandfaxcanbecarriedoutwithacopiermachine.These results contrastwith those obtained withexperiment 1 which included cleaning-service, eyeglass cleaning-service and shoes cleaningservice.
Fig.9.Theconceptlatticeofproducts.
Table3
Theresultsforexperimentsofproduct-servicesystem.
Problem Case(productandknownsolution) Bestfivematches
Product Service Experiment1(numericfeaturesonly) Experiment2(classfeaturereplaces weightandvolumefeatures)
Product-service Similarity Product-service Similarity
1 Washingmachine Repair Refrigeratorrepair 94 Refrigeratorrepair 90
Computerrepair 91.29 Computerrepair 89.82
Waterheaterrepair 89.92 Waterheaterrepair 88.45 Laserprinterrepair 89.85 Laserprinterrepair 87.88
LCDmonitorrepair 88.11 LCDmonitorrepair 87.27
2 Refrigerator Repair Waterheaterrepair 95.92 Waterheaterrepair 94.45
Washingmachinerepair 94 Computerrepair 91.56
Computerrepair 93.02 LCDmonitorrepair 90.43
LCDmonitorrepair 91.26 Washingmachinerepair 90
Laserprinterrepair 83.85 Digitalcalendar 82.12
3 Computer Repair LCDmonitorrepair 95.394 LCDmonitorrepair 94.19
Refrigeratorrepair 93.02 Refrigeratorrepair 91.56 Waterheaterrepair 93.01 Washingmachinerepair 89.82 Washingmachinerepair 91.29 Waterheaterrepair 89.51
Laserprinterrepair 85.21 Digitalcalendar 84.97
4 Laserprinter Repair Washingmachinerepair 89.85 Washingmachinerepair 87.88
Printingservice 88.4 Printingservice 87.23
Computerrepair 85.21 Computerrepair 82.87
Refrigeratorrepair 83.85 Refrigeratorrepair 81.88
Waterheaterrepair 83.83 Onlinekaraoke 80.63
5 LCDmonitor Repair Computerrepair 95.39 Computerrepair 94.19
Waterheaterrepair 95.34 Waterheaterrepair 92.98 Refrigeratorrepair 91.26 Refrigeratorrepair 90.43 Washingmachinerepair 88.11 Washingmachinerepair 87.27
Digitalcalendar 86.16 Digitalcalendar 85.59
6 Waterheater Repair Refrigeratorrepair 95.92 Refrigeratorrepair 94.45
LCDmonitorrepair 95.34 LCDmonitorrepair 92.98
Computerrepair 93.01 Computerrepair 89.51
Washingmachinerepair 89.92 Washingmachinerepair 88.45
Laserprinterrepair 83.83 Digitalcalendar 84.17
7 Handbag Repair Jewelryrepair 95 Jewelryrepair 94.15
Watchrepair 93.18 Watchrepair 92.33
Audiobook 78.20 Treadmilllease 78.39
LCDTVlease 77.63 LCDTVlease 77.3
Table3(Continued)
Problem Case(productandknownsolution) Bestfivematches
Product Service Experiment1(numericfeaturesonly) Experiment2(classfeaturereplaces weightandvolumefeatures)
Product-service Similarity Product-service Similarity
8 Jewelry Repair Handbagrepair 95 Handbagrepair 94.15
Watchrepair 94.08 Watchrepair 92.33
Jewelryrental 80.67 Jewelryrental 80.67
Handbagrental 79.77 Handbagrental 78.92
Downloadaudiobook 75.00 Treadmilllease 74.29
9 Watch Repair Jewelryrepair 94.08 Jewelryrepair 92.33
Handbagrepair 93.18 Handbagrepair 92.33
Refrigeratorlease 75.09 Refrigeratorlease 76.99
Jewelryrental 74.75 Sofalease 75.86
Handbagrental 73.85 Platformbedlease 74.25
10 Treadmill Lease Washingmachinelease 94.85 Washingmachinelease 92.95
LCDTVlease 90.04 LCDTVlease 88.37
Dryerlease 89.68 Dryerlease 87.78
Dishwasherlease 82.06 Dishwasherlease 79.26
Refrigeratorlease 81.12 Refrigeratorlease 79.22
11 Washingmachine Lease Treadmilllease 94.85 Dryerlease 93.67
Dryerlease 94.83 Treadmilllease 92.95
LCDTVlease 93.39 LCDTVlease 91.42
Refrigeratorlease 86.27 Dishwasherlease 84.31
Dishwasherlease 85.41 Refrigeratorlease 82.27
12 LCDTV Lease Washingmachinelease 93.39 Washingmachinelease 91.42
Treadmilllease 90.04 Treadmilllease 88.37
Refrigeratorlease 88.81 Refrigeratorlease 86.84
Dishwasherlease 88.33 Dryerlease 86.76
Dryerlease 88.22 Dishwasherlease 85.46
13 Sofa Lease Platformbedlease 91.8 Platformbedlease 90.05
Credenzaslease 85.29 Credenzaslease 82.69
Refrigeratorlease 85.20 Refrigeratorlease 81.89
Treadmilllease 79.075 Downloadaudiobook 77.43
Dishwasherlease 78.02 Treadmilllease 76.68
14 Dryer Lease Washingmachinelease 94.83 Washingmachinelease 93.67
Treadmilllease 89.68 Treadmilllease 87.78
LCDTVlease 88.22 LCDTVlease 86.76
Dishwasherlease 82.84 Dishwasherlease 81.41
Refrigeratorlease 81.1 Refrigeratorlease 77.6
15 Platformbed Lease Credenzaslease 93.49 Credenzaslease 90.89
sofalease 91.8 Sofalease 90.05
Dishwasherlease 86.22 Dishwasherlease 84.52
Refrigeratorlease 82.89 Musicdownload 84.33
Musicdownload 80.83 Downloadaudiobook 83.03
16 Refrigerator Lease LCDTVlease 88.81 LCDTVlease 86.84
Washingmachinelease 86.27 Washingmachinelease 82.27
Sofalease 85.19 Sofalease 81.89
Platformbedlease 82.89 Platformbedlease 80.29
Credenzaslease 82.45 Treadmilllease 79.22
17 Credenzas Lease platformbedlease 93.67 Platformbedlease 90.89
Sofalease 85.42 Musicdownload 82.83
Refrigeratorlease 82.42 Sofalease 82.69
Dishwasherlease 81.5 Downloadaudiobook 81.53
Musicdownload 80.83 Refrigeratorlease 78.95
18 Dishwasher Lease LCDTVlease 88.33 LCDTVlease 85.46
Platformbedlease 86.22 Platformbedlease 84.52
Washingmachinelease 85.41 Washingmachinelease 84.31
Dryerlease 82.84 Dryerlease 81.41
Treadmilllease 82.06 Treadmilllease 79.26
19 Luggagebox Rental GPSrental 94.85 GPSrental 91.55
Scanningservice 87.5 Scanningservice 84.2
Cleaningservice 84.95 Cleaningservice 80.83
Videocamerarental 83.67 Eyeglasscleaningservice 79.95 EyeglassCleaningservice 83.25 Videocamerarental 79.47
20 VideoCD/DVD Rental Entertainmentbookrental 94.71 Multimediaondemand 92.27
Faxservice 92.63 Entertainmentbookrental 91.21
Multimediaondemand 92.27 Faxservice 90.8
Onlinemagazine 90.84 Onlinemusic 88.4
Onlinemusic 88.4 Onlinemagazine 87.34
21 Eveningdress Rental Handbagrental 85.38 Handbagrental 85.66
Jewelryrental 84.48 Jewelryrental 85.66
Videogamerental 73.73 Videogamerental 72.47
Photographerservice 70.28 Handbagrepair 66.33
Table3(Continued)
Problem Case(productandknownsolution) Bestfivematches
Product Service Experiment1(numericfeaturesonly) Experiment2(classfeaturereplaces weightandvolumefeatures)
Product-service Similarity Product-service Similarity
22 Entertainmentbook Rental VideoCD/DVDrental 94.71 VideoCD/DVDrental 91.21
Scanningservice 90.29 Scanningservice 87.66
Onlinemagazine 88.4 Onlinemagazine 87.23
Faxservice 87.34 Faxservice 85.84
Multimediaondemand 86.98 Eyeglasscleaningservice 83.88
23 Videogame Rental Entertainmentbookrental 78.18 VideoCD/DVDrental 75.73
Jewelryrental 76.00 Entertainmentbookrental 74.68
VideoCD/DVDrental 75.73 Handbagrental 72.50
Handbagrental 75.10 Jewelryrental 72.50
Audiobook 73.77 Eveningdressrental 72.47
24 Jewelry Rental Handbagrental 99.1 Handbagrental 98.25
Eveningdressrental 84.48 Eveningdressrental 85.66
Jewelryrepair 80.67 Jewelryrepair 80.67
Videogamerental 76.00 Handbagrepair 74.82
Handbagrepair 75.67 Watchrepair 73
25 Handbag Rental Jewelryrental 99.1 Jewelryrental 98.25
Eveningdressrental 85.38 Eveningdressrental 85.66
Jewelryrepair 79.77 Jewelryrepair 78.92
Handbagrepair 76.57 Handbagrepair 76.57
Videogamerental 75.10 Watchrepair 73
26 GPS Rental Luggagerental 94.85 Luggagerental 91.55
Eyeglasscleaningservice 88.4 EyeglassCleaningservice 85.6
Cleaningservice 88.3 Cleaningservice 85.08
Videocamerarental 87.02 Videocamerarental 84.42
Laminatingservice 84.15 Laminatingservice 80.65
27 DV(videocamera) Rental Photographerservice 95.9 Photographerservice 95.9
Cleaningservice 88.72 EyeglassCleaningservice 85.12 Eyeglasscleaningservice 87.02 Cleaningservice 84.6
GPSrental 87.02 GPSrental 84.42
Luggagerental 83.67 Laundryservice 82.6
28 Faxmodem Payperserviceunit Scanningservice 92.95 Scanningservice 92.08
VideoCD/DVDrental 92.63 VideoCD/DVDrental 90.8
Onlinedictionary 89.1 Onlinedictionary 88.77
Laundryservice 88.72 Laundryservice 87.65
Eyeglasscleaningservice 88.7 EyeglassCleaningservice 87.03
29 Printer Payperserviceunit Laundryservice 93.72 Copyingservice 92.53
Laminatingservice 92.05 Laundryservice 92.25
Copyingservice 91.67 Laminatingservice 91.75
Laserprinterrepair 88.4 Laserprinterrepair 87.23 Cleaningservice 87.9 Eyeglasscleaningservice 87.03
30 Photostat Payperserviceunit Laundryservice 97.95 Laundryservice 93.95
Printingservice 91.67 Printingservice 92.53
Cleaningservice 88.07 Eyeglasscleaningservice 89.23 Eyeglasscleaningservice 87.97 Scanningservice 87.01
Shoescleaningservice 87.26 Faxservice 86.6
31 Scanning Payperserviceunit Eyeglasscleaningservice 95.75 Eyeglasscleaningservice 92.95
Faxservice 92.95 Faxservice 92.08
Laminatingservice 91.5 Laminatingservice 89.17
Entertainmentbookrental 90.29 Entertainmentbookrental 87.66
VideoCD/DVDrental 87.85 Copyingservice 87.01
32 Laminating Payperserviceunit Eyeglasscleaningservice 95.75 Eyeglasscleaningservice 92.95
Printingservice 92.05 Printingservice 91.75
Scanningservice 91.5 Scanningservice 89.17
Laundryservice 85.77 Laundryservice 86.33
faxservice 84.45 Copyingservice 85.45
33 Washingmachine Payperserviceunit Copyingservice 97.95 Copyingservice 93.95
Printingservice 93.72 Eyeglasscleaningservice 92.68
Cleaningservice 90.12 Printingservice 92.25
EyeglassCleaningservice 90.02 Faxservice 87.65
Faxservice 88.72 Cleaningservice 86.9
34 Cleaningproduct Payperserviceunit Laundryservice 90.12 Laundryservice 86.9
VideocameraRental 88.72 Eyeglasscleaningservice 85.08
EyeglassCleaningservice 88.3 GPSrental 85.08
GPSrental 88.3 Copyingservice 84.85
Copyingservice 88.07 Videocamerarental 84.6
35 Shoescleaning Payperserviceunit Copyingservice 87.26 Eyeglasscleaningservice 91.53
Laundryservice 85.21 Copyingservice 90.36
EyeglassCleaningservice 82.96 Laundryservice 89.71
Printingservice 78.925 Laminatingservice 85.88
In order to corroborate the influence of the semantic similarity,anadditionalexperimentwasconducted(experiment 3). Experiment 3 excluded the numeric product features of volume and weight as well as the semantic feature. For this evaluation,we counted the cases in the best five results that were common to those in experiment 1. In other words, the ideal number of common cases is 5. These results are summarizedinTable 4.Thepresenceofthesemanticsimilarity
measureinexperiment2resultedinanaverageof4.32common cases,while itsabsenceinexperiment3resultedinanaverage of 3.77. This means that in the absence of data for product volumeandweight, theuseof thesemanticfeatureshows an improvementofalmost15%comparedtonotusingit.Fromthis, itcan beconcluded that ontology-basedsemantic similarities havetheabilitytoemulate(atleasttosomeextent)thenumeric productfeatures.
Table3(Continued)
Problem Case(productandknownsolution) Bestfivematches
Product Service Experiment1(numericfeaturesonly) Experiment2(classfeaturereplaces weightandvolumefeatures)
Product-service Similarity Product-service Similarity
36 Eyeglasscleaning Payperserviceunit Laminatingservice 95.75 Laminatingservice 92.95
Scanningservice 95.75 Scanningservice 92.95
Laundryservice 90.02 Laundryservice 92.68
Faxservice 88.7 Copyingservice 89.23
GPSrental 88.4 Printingservice 87.03
37 DV(videocamera) Payperserviceunit Videocamerarental 95.9 Videocamerarental 95.9
Cleaningservice 84.62 Eyeglasscleaningservice 81.02 EyeglassCleaningservice 82.92 Copyingservice 80.55
GPSrental 82.92 Cleaningservice 80.5
Copyingservice 80.88 GPSrental 80.32
38 MusicCD
(onlinemusic)
Functionalresult Onlinenewspaper 97.16 Multimediaondemand 96.13
Multimediaondemand 96.13 Onlinenewspaper 94.36
Onlinemagazine 94.71 Onlinemagazine 91.91
Onlinedictionary 91.93 Onlinedictionary 90.18
VideoCD/DVDrental 88.4 VideoCD/DVDrental 88.4
39 Magazine Functionalresult Multimediaondemand 98.58 Multimediaondemand 95.08
Onlinenewspaper 94.71 Onlinenewspaper 94.71
Onlinemusic 94.71 Onlinedictionary 92.63
Onlinedictionary 94.38 Onlinemusic 91.91
VideoCD/DVDrental 90.84 Onlinekaraoke 88.78
40 Karaoke Functionalresult Multimediaondemand 91.67 Onlinemagazine 88.78
Onlinemagazine 90.24 Multimediaondemand 88.7
Onlinemusic 87.8 Onlinedictionary 86
Onlinedictionary 87.47 Onlinemusic 85.63
Onlinenewspaper 84.96 Onlinenewspaper 82.79
41 MusicCD
(musicdownload)
Functionalresult Downloadaudiobook 98.7 Downloadaudiobook 96.95
Platformbedlease 80.83 Platformbedlease 84.33
Credenzaslease 80.23 Credenzaslease 82.83
Dishwasherlease 78.38 Dishwasherlease 78.92
Onlinemusic 78.03 Onlinemusic 78.03
42 VideoCD/DVD(multimedia ondemand)
Functionalresult Onlinemagazine 98.58 Onlinemusic 96.13
Onlinemusic 96.13 Onlinemagazine 95.08
Onlinedictionary 95.8 Onlinedictionary 94.05
Onlinenewspaper 93.29 VideoCD/DVDrental 92.27
VideoCD/DVDrental 92.27 Onlinenewspaper 90.49
43 MAP Functionalresult Onlinemagazine 85.4 Onlinemagazine 83.5
Onlinedictionary 83.98 Onlinedictionary 83.13
Multimediaondemand 83.98 Multimediaondemand 82.08
Luggagerental 80.79 Luggagerental 78.63
Onlinenewspaper 80.11 Onlinenewspaper 78.21
44 Newspaper Functionalresult Onlinemusic 97.16 Onlinemagazine 83.5
Onlinemagazine 94.71 Onlinedictionary 83.13
Multimediaondemand 93.29 Multimediaondemand 82.08
Digitalcalendar 89.12 Luggagerental 78.63
Onlinedictionary 89.09 Onlinenewspaper 78.21
45 Dictionary Functionalresult Multimediaondemand 95.8 Multimediaondemand 94.05
OnlineMagazine 94.38 Onlinemagazine 92.63
Onlinemusic 91.93 Onlinemusic 90.18
Faxservice 89.1 Faxservice 88.77
Onlinenewspaper 89.09 Onlinenewspaper 87.3
46 Calendar Functionalresult Onlinenewspaper 89.12 Onlinenewspaper 87.37
Onlinemusic 86.27 LCDmonitorrepair 85.59
LCDmonitorrepair 86.16 Computerrepair 84.97
Onlinedictionary 84.51 Onlinemusic 84.52
Computerrepair 84.40 Waterheaterrepair 84.17
47 Book Functionalresult Musicdownload 98.7 Musicdownload 96.95
Platformbedlease 79.53 Platformbedlease 83.03
Credenzaslease 78.93 Credenzaslease 81.53
Handbagrepair 78.20 Dishwasherlease 78.78
8. Relatedwork
Severaleffortsarereportedontheuseofontologiesinproduct design.OneinterestingexampleistheworkofPatiletal.[48]who describeanontologythatisbasedondefinitionsfromtheNIST’s CoreProduct Model.They describea methodology for building artifact ontologies which is based on the identification of subclassesofartifact,feature,assembly,andotherclasses.Inthe ontology development process,each artifact is characterized in termsofitsform,function,andbehavior(theimplementationof thefunction).
Annamalaiet al.[49]defineageneralframeworkfor product-servicesystemsthatisfacilitated bymeansofanontology.The terminologiesandsemanticsarebasedoneighttop-levelclasses thatcoverproductlifecycleandthesupportingelementssuchas
stakeholderinvolvement.Theontologyisdevelopedusingfindings fromliteraturereviewandtheopinionofdomainexperts.
Intheareaofproductcustomization,Tsengetal.[50]presenta CBRsystemtosupportconceptualproductdesign.Intheirwork,a numericsimilaritymeasureis combinedwithpart-whole infor-mationthathasatreerepresentation.Anothersimilarworkisthat of Cobb and Agogino [51] who developed a CBR system for designing Micro-Electro-MechanicalSystems (MEMS).They dis-cusstheresultsofacase-retrievalexperimentinwhichMEMSare described in terms of functional and structural features. These featuresarenumeric,whichsuggestthatcaseretrievaliscarried outbymeansofanumericsimilarity.
Inanattempttogeneratenewproductideas,Wuet al.[52] propose a CBRsystem in which a product is represented as a numericvectorconsistingof87elements.Eachelementrepresents aproductattribute.Theproductattributesareorganizedintofive dimensions: interface modality, task, physical feature, environ-ment,andusers.Someoftheattributesintheinterfacemodality resembletheuseoftheparticipationrelationdefinedinISO15926 suchasspecifyingthepartsofthebodyinvolvedin[theuseof]a given product. The task dimension represents the tasks to be performedbytheuserthroughtheuseoftheproduct.Attributesin thisdimensionareequivalenttospecificprocessesassociatedtoa product.Thephysicaldimensionisforattributessuchasproduct sizes.Environmentincludesattributessuchasindoororoutdoor places.Finally,attributesintheuserdimensioncharacterizethe userintermsofgender,age,etc.Everyattributeintheproduct vector requires a value that represents the relevancy to that attribute.
Linet al.[6]proposetheuseofCBRtosupportthedesignof product service systems (PSS). Specifically, their CBR selects servicestrategiesforagivenproduct.Acaseisdescribedinterms of12 featureswhicharegrouped intothree categories,namely, user behavior, product, and environmental environment. User behaviorisspecifiedintermsofplaceofusage,andfrequencyof usage.Theproductisspecifiedintermsoffeaturesdescribingits fashioncycle,volume,weight,usefullife,price, andsubsequent expenditure.ExternalenvironmentisdefinedintermsofGDPper capita, population density, area of territory, and temperature range. Eachfeature isquantifiedusing integervalues. Thecase similarityisobtainedbyusingaweightedsummation ofallthe featuresimilarities.Theweightsaredeterminedbymeansofthe analytichierarchyprocess(AHP).
IntheareaofWebservices,Bramantoroet al.[13]proposea similaritymeasurethatquantifiesthesemanticdistancebetween classes inanontologyoftheproducts thataredeliveredbythe services. Their work is motivated by limitations of other approachesinwhichonlycertainsuperclass–subclasslinkswere takenintoaccount.Theirapproachwasbasedonthepathlength, numberofdownwardedgescountedbetweentwoclasses,andthe numberofcommonclosestancestors.Itisinterestingtonotethat despitethefactthatsomesemanticmeasuresalreadyexistedthey wereapparentlyunknowninthatdomain.
9. Conclusions
This paper presented ontology-based semantic similarity measures that determine the degree of likeness between two classes.Themaindistinguishingaspectoftheproposedapproachis the useof ontologies obtained withFCA coupled with feature-basedsimilarities.Resultsofthenumericexperimentsshowedthat in all cases,the proposedsemantic measuresperformed better thanthesimilaritiesofWu-PalmerandLin.
Intheelectricapplianceexperiment,afterremovingtheleast performingpairs(electrickettle,televisionset)and(electrickettle, electricoven),thecorrelationsawanincreaseofapproximately
Table4
Comparisonofidenticalcases.
25%.Thereasonmightbethatbothtelevisionsetandelectricoven were characterized by processes which are unfamiliar to the commonuser.Forexample,toasterwascharacterizedasadevice thatusesinfraredradiation. Inthis case,infraredradiation was consideredasapartofheating,whichisdirectlyrelatedtotoasting bread. Similarly, TV set was defined as a device that receives televisionsignals.
Whenotherdeviceswerecharacterizedintermsofprocesses andparticipatingobjectsthatweremorefamiliartothecommon user, the calculated similarities were close to the human judgments.However, albeit importantto thedesigners, from a userpointofview,subprocessesthatarenotdirectlyperceivedby theusers(i.e.themechanismwithwhichaproductachievesits givenfunction)areprobablynottakenintoaccount.Thiscouldbea limitation of the questionnaire approach for evaluating the similarities.
A CBRsystemfor productservicesystemsdemonstrated the effectivenessoftheproposedsimilaritymeasures.IntheCBRcase study,thecombinationoftheontologyandthesemanticsimilarity provedusefulwhensomedetailssuchasweightandvolumeare notavailable.Therefore,thedesignercanberelievedbyneeding lessdatatodefineagivendesignproblem,whichisparticularly importantduringtheconceptualstageofthedesign.
Nevertheless, in a few instances the proposed approach resultedinmismatches.Thiscouldbeduetothelackofattributes intheFCAcontexttable.Forexample,theadditionofattributes that emphasize thedifference between software and hardware productscouldreducethenumberoffalsepositivesforproblem2. AkeyelementintheproposedapproachistheuseofFCA.From aninformationmodelingpointofview,theuseofformalconcept analysisisusefulbutthedevelopmentofthecontexttablehasa largedegreeoffreedom.Specifically,theselectionofattributesin thecontext tableof theFCA analysis plays an important role. Therefore,theselectionofattributesshouldbebasedonanexplicit guideline.Inthispaper,theattributeswereselectedbasedonthe assumption that every product performs or is-involved-in processes (or activities using the ISO 15926 terminology). Therefore,subclassesofphysicalobjectarecharacterizednotonly by the mereology of the objects, but also by the processes associatedtothem.Aclassofprocessisinturncharacterizedin termsoftheobjectsthataretransformed(inputs),objectsthatare produced(outputs),otherparticipatingobjects,andthemereology oftheprocess.
Finally, the results of the correlation between the different semanticsimilaritiesandhumanjudgmentorWeb-basedsearch suggestthatmultiplesimilaritymeasurescanbeusedasawayto validateontologies.Thereasonisthattheaccuracyoftheontology directlyinfluencesthecorrelationvalues.
Acknowledgments
TheauthorswouldliketothankthemembersoftheIndustrial SystemsEngineeringGroupofToyohashiUniversityofTechnology fortheirparticipationinthehumanjudgmentevaluation.Wealso expressourappreciationtoYusukeTsujiokaandYukihikoTakada fortheirhelpinthesoftwareimplementation.Wearegratefulto HongcaiWangfortranslatingthedatausedinthecasestudyandto FarizaMohamadforherhelpintheelaborationofthe question-naire.Theauthorsarealsoindebtedtothereviewerandeditorof thisjournalfortheirhelpfulcommentsandsuggestions.
AppendixA. Formalconceptanalysis
Formalconceptanalysis(FCA)canbeusedtodesignontologies fromalistofpotentialclassesandtheirrespectiveattributes.FCAis ananalysistechniqueforinformationprocessingbasedonapplied
latticeandordertheorythatcanbeusedtogeneratetaxonomies. InFCA,informationisorganizedintermsofasetofformalobjects O,asetofformalattributesA,andasetofbinaryrelationsYOA containingall pairs ho;ai2Y suchthat theobject o2O hasthe
attribute a2A. For our purposes, the formal objects represent candidateclassesforanontology.
Informationaboutthesethreesetsistypicallysummarizedbya contexttablesuchastheoneshowninFig.3.Inacontexttable,the objectsarelistedinthefirstcolumnandtheattributesinthefirst rowofthetable.
AformalconceptisdefinedasthepairhOi;Aiisuchthat:
1.OiO,AiA.
2.EveryobjectinOihaseveryattributeinAi.Conversely,Aiisthe setofattributessharedbyalltheobjectsinOi.
3.Foreveryobjectp2OthatisnotinOi,thereisanattributeinAi that pdoesnothave.
4.ForeveryattributeinAthatisnotinAi,thereisanobjectinOi thatdoesnothavethatattribute.
5.Formalconceptscanbepartiallyorderedintoalattice,suchthat aconceptsubsumesanotherconcept.Fig. 4showsthelattice obtainedwiththedataofFig. 3.Several lattice-construction algorithmsareavailablesomeofwhichhavebeensuccessfully implementedinseveralapplications.
References
[1]H.A.ElMaraghy,W.A.ElMaraghy(Eds.),AdvancesinDesign,Springer,Berlin, 2006,p.31.
[2]M.L.Maher,Processmodelsfordesignsynthesis,AIMagazine11(4)(1990). [3]S.Szykman,J.Racz,C.Bochenek,R.D.Sriram,Web-basedsystemfordesignartifact
modeling,DesignStudies21(2)(2000)145–165.
[4]S.K.Ong,A.Y.Nee,Q.L.Xu,Designreuseforembodimentanddetaildesign,in: DesignReuseinProductDevelopmentModeling,Singapore,WorldScientificPub. CoInc.,2009,pp.237–247.
[5]J.J.Shah,D.K.Jeon,S.D.Urban,P.Bliznakov,M.Rogers,Databaseinfrastructurefor supportingengineeringdesignhistories,Computer-AidedDesign28(5)(1996) 347–360.
[6]K.Lin,L.Shih,S.Lu,Y.Lin,Strategyselectionforproductservicesystemsusing case-basedreasoning,AfricanJournalofBusinessManagement4(6)(2010)987– 994.
[7]M.M. Richter,Similarity, in:Case-BasedReasoning forSignals and Imaging, SpringerVerlag,Berlin,Heidelberg,2007,pp.25–90.
[8]P.Cunningham,Ataxonomysimilaritymechanismsforcase-basedreasoning, UCD-CSI(2008).
[9]A.Maedche,S.Staab,ComparingOntologies—SimilarityMeasuresanda Compar-isonStudy, InstituteAIFB,UniversityofKarlsruhe,Karlsruhe,Germany,2001. [10]D.Lin,Aninformation-theoreticdefinitionofsimilarity,Proc.15thInt.Conf.on
MachineLearning,Madison,Wisconsin,1998.
[11]V.Cross,X.Hu,Usingsemanticsimilarityinontologyalignment,Sixth Interna-tionalWorkshoponOntologyMatching,2011.
[12]V.Olehschuk,A.Pedersen,Ontologybasedsemanticsimilarityfordocuments, 14thInternationalWorkshoponDatabaseandExpertSystemApplication,Prague, 2003.
[13]A.Bramantoro,S.Krishnaswamy,M.Indrawan,Asemanticdistancemeasurefor matchingwebservices,LCNS3807,WISE2005Workshops,NewYork,2005. [14]J.S.Madin,Advancingecologicalresearchwithontologies,TrendsinEcology&
Evolution23(3)(2008)159–168.
[15]R.Batres,M.West,D.Leal,D.Price,K.Masaki,Y.Shimada,T.Fuchino,Y.Naka,An upperontologybasedonISO15926,ComputersandChemicalEngineering31 (2007)519–534.
[16]H.Haav,Asemi-automaticmethodtoontologydesignbyusingFCA,Proc.Fourth Int.Conf.onConceptLatticesandTheirApplications,(2004),pp.13–24. [17]R.Batres,Y.Shimada,T.Fuchino,Asemanticapproachforincidentdatabase
development,IChemESymposiumSeriesNo.155,(2010),pp.477–484. [18]ISO-TC-184,ISO-TC-184/SC4,SO10303, ‘‘Industrialautomationsystemsand
integration—productdatarepresentationandexchange—Part11:Description methods:TheEXPRESSlanguagereferencemanual,in:ISOTC184/SC4, 1994. [19]D.Leal,ISO15926‘‘Lifecycledataforprocessplant’’:anoverview,Oil&Gas
ScienceandTechnology60(4)(2005)629–637.
[20]Z.Wu,M.Palmer,Verbssemanticsandlexicalselection,32ndAnnualMeetingof theAssociationsforComputationalLingustics,NewMexico,1994.
[21]A.Tversky,Featuresofsimilarity,PsychologicalReview84(4)(1977)327–352. [22]L.R.Dice, Measuresofthe amountofecologicassociationbetween species,
Ecology26(3)(1945)297–302.
[24]S.Yevtushenko,ConceptExplorer,OpenSourceJavaSoftware, 2009[Online]. Available:hhttp://sourceforge.net/projects/conexp,Release1.3i.
[25]M.Uschold,M.King,Towardsamethodologyforbuildingontologies,Workshop onBasicOntologicalIssuesinKnowledgeSharing,1995.
[26]M.Grueninger,M.S.Fox,Methodologyforthedesignandevaluationofontologies, WorkshoponBasicOntologicalIssuesinKnowledgeSharing,1995.
[27]N.F.Noy,D.McGuinness,OntologyDevelopment101:AGuidetoCreatingYour FirstOntology,in:StanfordKnowledgeSystemsLaboratoryTechnicalReport KSL-01-05.Standford,USA, 2001.
[28]M.Fernandez-Lopez,A.Gomez-Perez,N.Juristo,Methontology:FromOntological ArtTowardsOntologicalEngineering, AAAITechnicalReportSS-97-06,1997. [29]A.Gomez-Perez,Evaluationoftaxanomicknowledgeinontologiesand
knowl-edgebases,12thWorkshoponKnowledgeAcquisition,Modelling,and Manage-ment(KAW‘99),Alberta,Canada,1999.
[30]R.Stevens,C.A.Coble,S.Bechhofer,Ontology-basedknowledgerepresentationfor bioinformatics,BriefingsinBioinformatics1(4)(2000)398–414.
[31]J.Han,M.Kamber,J.Pei,Gettingtoknowyourdata,in:DataMining:Conceptsand Techniques,MorganKaufmann,UnitedStatesofAmerica,2013p.71. [32]M.A.Rodrı´guez,M.Egenhofer,Determiningsemanticsimilarityamongentity
classesfromdifferentontologies,IEEETransactionsonKnowledgeandData Engineering15(2003)442–456.
[33]D.vanderWeken,M.Nachtegael,E.E.Kerre,Usingsimilaritymeasuresand homegeneity for the comparison ofimages, Image and VisionComputing (2004)695–702.
[34]StanfordCenterforBiomedicalInformaticsResearch.,[Online].Available: [35]Web-Ontologyworkinggroup,OWLwebontologylanguage, seconded.,2004
hhttp://www.w3.orgi.
[36]Jena,HPLabsSemanticWebToolkit.[Online].,2013Available: hhttp://jena.sour-ceforge.net/i.
[37]R.E.Schafrik,Unitmanufacturingandassemblyprocess,in:J.Lee(Ed.),Modern Manufacturing,MechanicalEngineeringHandbook,CRCPressLLC,BocaRaton,FL, 1999,pp.13-8–13-34.
[38]S.Kalpakjian,S.R.Schmid,ManufacturingEngineeringandTechnology,sixthed., Prentice-Hall,UpperSaddleRiver,NJ,2010.
[39]B.S.NagendraParashar,R.K.Mittal,ElementsofManufacturingProcesses, Pren-tice-HallofIndiaPrivateLimited,NewDelhi,India,2007.
[40]E.P.Degarmo,J.T.Black,R.A.Kohser,MaterialsandProcessesinManufacturing, 10thed., Wiley,Hoboken,NJ,2010.
[41]B.Handley,D.M.Marshall,C.Coon,PrinciplesofEngineering, Delmar,2012. [42]M.C.Finishing,BlastingTechnicalInformation[Online], 2012Available:hhttp://
mcfinishing.com/resources/blastingtech.pdfi[Accessed2012].
[43]R.Cilibrasi,P.Vitanyi,The Googlesimilarity distance,IEEETransactionson KnowledgeandDataEngineering19(3)(2007)370–383.
[44]D.Giustini,HowBigisGoogleScholar?500MillionDocuments?, 2006[Onli-ne] hhttp://blogs.ubc.ca/googlescholar/2006/04/how-big-is-google-scholar-500-mil-lion-documents/i(Available)[Accessed25May2012].
[45]NationalScienceBoard,Scienceand EngineeringIndicators2010, National ScienceFoundationNSB10-01,Arlington,VA,2010.
[46]J.Kolodner,R.L.Simpson,Case-basedreasoning,in:K.H.Lin,L.H.Shih,S.S.Lu,Y.T. Lin(Eds.), StrategySelection for ProductService Systems UsingCase-based Reasoning.AfricanJournalofBusinessManagement,vol.4(6),2010. [47]A.Pease,SuggestedUpperMergedOntology(SUMO)[Online], 2010Available:
hhttp://www.ontologyportal.org/i[Accessed2010December2010].
[48]L.Patil,D.Dutta,R.D.Sriram,Ontologyformalizationofproductsemanticsfor productsemanticsforproductlifecyclemanagement,NISTIR7274(November) (2005).
[49]G.Annamalai,R.Hussain,M.Cakkol,R.Roy,AnOntologyforProduct-Service Systems, CranfieldUniversity,Cranfield,Bedfordshire,UK,2011.
[50]H.-E.Tseng,C.-C.Chang,S.-H.Chang,Applyingcase-basedreasoningforproduct configurationinmasscustomizationenvironments,ExpertSystemswith Appli-cations(2005)913–925.
[51]C.L.Cobb,A.M.Agogino,Case-basedreasoningforthedesignof micro-electro-mechanical systems,ASME2006International DesignEngineeringTechnical Conferences & Computers and information inEngineering Conference, PA, USA.,2006.
[52]M.C.Wu,Y.F.Lo,S.H.Hsu,Acase-basedreasoningapporachtogeneratingnew productideas,InternationalJournalofAdvancedManufacturingTechnology30 (2006)166–173.
SuriatiAkmalisagraduatestudentintheDepartment ofMechanicalattheToyohashiUniversityof Technol-ogy.ShereceivedherBSinManufacturingEngineering fromIIUMalaysiain2003.In2006,shegraduatedwith aMScinGlobalProductionEngineeringfromTUBerlin andwassubsequentlyappointedtothepostoflecturer at Manufacturing Department, University Teknikal MalaysiaMelaka(UTeM).Currently,sheisonleave fromUTeMandworkingtowardherdoctoraldegreein IndustrialSystemEngineering.Herresearchinterests includesemanticsimilaritymethodsforproductsand processes,processplanningandproductdevelopment.
Li-HsingShihisaprofessorofResourcesEngineeringat NationalChengKungUniversity,Taiwan.HeheldBS andMSofCivilEngineeringatNationalChengKung UniversityandMSandPhDonIndustrialEngineering fromUniversityofOklahoma.Hismajorresearchareas includedesignforrecycling,reverselogistics, sustain-ableinnovationmanagement,andsustainableproduct andservicedevelopment.
