This document presents a framework for innovating new web services by utilizing existing web service networks. The framework involves constructing a network of semantically annotated web services, finding candidate links between unconnected concepts in the network, filtering out non-meaningful candidates, expanding candidate links with related concepts, and evaluating candidates collectively based on the network's small-worldness, scale-freeness, and correlation of node degrees. The framework was experimentally applied to a collection of 753 semantically annotated web services, and the results demonstrated that the constructed networks exhibited desirable small-world and scale-free properties. Future work could employ web service composition and measure the added value of suggested services using quality of service metrics.

1. ShahabMokarizadeh,RoyalInstituteofTechnology,Sweden
PeepKungas,UniversityofTartu,Estonia
MihhailMatskin,RoyalInstituteofTechnology,Sweden
UtilizingWebservicesNetworks
forWebserviceInnovation
ICWS-201427June-2July2014

2. Outline
2
Introduction
WebserviceInnovationPatterns
FrameworkforWebserviceInnovation
ExperimentalResults
ConclusionandFuturework
ICWS-201427June-2July2014

3. Introduction
3
IncreasingpresenceandadoptionofWebservicesmotivatedmany
worksinWebservicerecommendationsolutionstosuggestthebest
alternativesforthegivenuserrequirements
Butonemajorchallenge:IdentifyingMissingbutpotentiallyValuable
Webservice(tobeimplemented):
HowtofindmissingWebservices?
Webserviceinnovation
Howtomeasureadded-valueoftheinnovatedWebservice?
Theimportanceofconsideredproblem
ThisworkproposesapartialsolutionforWebserviceinnovation
problem
ICWS-201427June-2July2014

4. WebserviceInnovationPatterns
4
Webserviceinnovation:“Creatingavaluableservicewhichisdifferent
thanexistingones”
NewWebservicesareemergingwithaccordanceononehefollowing
patterns:
IntroducedWebserviceisconceptuallysimilartoexistingones,
differencelinesinQoS
CurrencyexchangeservicesprovidedbyCentralBankofAmericaandForex
TheintroducedWebserviceprovidesaddedvaluepropositionontop
ofexistingones
PriceRunnerservicewhichcollectsandsortswebshoppingservicesform
differentproviders
TheintroducedWebservicesenjoysanovelbusinessmodelina
particulardomain
MapQuestTrafficAPI:real-timetrafficinformation
ICWS-201422-27Aug2011

5. WebserviceInnovationFramework
5
1
•ConstructingWebservicenetwork
2
•Findingcandidates
3
•Filteringnon-meaningfulones
4
•Expandingcandidateswithsemantically
relevantconcepts
5
•Networkdrivencollectiveevaluation
ICWS-201427June-2July2014

6. SemanticWebserviceNetworkConstruction
6
WhyWebservicenetwork?
Thenetworkcharacteristicscanbeutilizedforlinkpredictionand
evaluation
WeconsidersemanticallyannotatedWebservices:
Semanticannotationhelpstoidentifynon-meaningfulsuggested
newservices
Semanticabstractsthefunctionality
Mainlyannotationofinput(s)andoutput(s)ofWebservice
operationisneeded

7. NetworkConstructionStages
7

8. FindingCandidateLinks
8
Candidatelinks:initiallysetofalllinksconnectingpairofconcepts
whicharenotdirectlyconnectedinthenetwork.
7446,573,241716,51,,,,,,,,,,,SSSSSSSSSSSS

9. RemovingRedundantCandidateLinks
9
Next,removeallpairsthatreachablefromeachother(eitherside):
FindingallreachablenodeswithinKstepsinadirectedgraph
representedbyadjacencymatrixGrequires:
FindingallreachablenodesrequiresD-1timesmatrixmultiplication,
whereDisdiameterofthenetwork
But,WebservicenetworkisaSmall-worldnetwork,
Atmost:
7446,5,,,SSSS
K
GGGM...2
6K

10. Filteringnon-meaningfulcandidatelinks
10
Acandidatelinkismeaningfulifthelinkissemanticallysimilarto
existingonesaccordingtolocalknowledge.
Localknowledge:theontologyusedforWebserviceannotation+
thesmallworldmatrix:
Wemeasuresemanticdistancebetweencandidatelinksandthe
existingones,inthetoynetwork,compare:and
witheachof:
-
-
-
-
62
...GGGM
46,5,SS74,SS
3,22,1,SS
76,5,SS
43,2,SS
6,53,2,SS

11. SemanticSimilarityFinding
11

12. LinkExpansion
12
Sofar,eachlinkonlyassociatestwoconcepts
Weexpandtheconceptsinbothsidesofalinkwithagroupof
semanticallysimilarconcepts:
CityTaxisexpandedto
(City,Street,ZipCode)(Tax,Income)
Givencorpusofservicedescriptions,groupofsemantically
relevantconceptscanbefoundusingTopicModelingtechniques
Weusedourownclusteringmethodpresentedat:
“Ontologyacquisitionfromwebservicedescriptions”.SAC2013,p.325-
332,ACM,2013

13. NetworkDrivenCollectiveEvaluation
13
Amajorprobleminlinkpredictionevaluationisthataprior
probabilityofalinkistypicallyquitesmallduetosparsityin
dataset.
Makesitdifficulttoquantifythelevelofconfidenceinthepredictions
Aplausiblesolution:performcollectivelinkpredictionand
evaluation(insteadofindividualevaluationofeachlink)
Weadoptedanetworkdrivencollectiveevaluationmethod
whichconsidersthreemetrics:
•Small-worldness
•Scale-freeness
•Correlationdegreeofnodes

14. 14
Smallworldnetworksareidentifiedbycomparingthemwithsimilar
randomnetworks:
1.LRandom≤LActualL:ShortestPathLength
2.CRandom<<CActualC:ClusteringCoefficient
Sindex:SmallworldnessIndex
Inotherwords:
>1,λ>1,Sindex>1
Small-worldness
Small-worldnessscaleslinearlywith
networksize.

15. Scale-freeness
15
Fittedtopower-lawfunctionxcy.
Manynodeswithfewlinks
#oflinks(M)(log)
# of nodes with M links (log)
Afewnodeswithmanylinks

16. 16
CorrelationDegreeofNodes
PositiveCorrelation:ifnodeswithhighnumberofconnection
tendtobeconnectedwithothernodeswhichalsohavemany
links.
Observedinsocialnetworks,networkofactors,etc.
NegativeCorrelation:ifthepreferenceistoattachtothosehaving
smallquantityofconnections.
Observedintechnologicalandbiologicalnetworks:Internet,protein
interactions,manyofthetechnologicalnetworks.

17. ExperimentalResults
17
SAWSDL-TC1collectioncontaining753semanticallyannotatedWSDLs
Twodistancethresholdforfilteringnon-meaningfullinks:
1:onlydirectsiblings(conceptswithimmediatecommonparents)
2:bothdirectandindirectsiblings(conceptswithnotimmediate
commonancestorsthatcanbereachedinmaximumtwosteps)
Network#nodes#links
Original2252230
Original+Threshold12582348
Original+Threshold22603113
1-SemWebCentral.org.http://projects.semwebcentral.org/projects/sawsdl-tc/

18. 18
CollectiveNetworkEvaluation
Weexpectthatconstructednetworks(Original+Threshold1)
and(Original+Threshold2)demonstratethesmall-woldness,
scale-freenessandnegativecorrelationdegreeonnodes.
NetworkLCSindexPD
OriginalActual
Random-ER
2.2832
2.7725
0.3942
0.0699
6.84511.39-0.1072
Original+Threshold1Actual
Random-ER
2.8166
2.7246
0.392
0.0704
5.38281.38-0.1032
Original+Threshold2Actual
Random-ER
2.5776
2.4937
0.3683
0.0946
3.76561.31-0.1666
L:shortestpathlengthP:powerlawdegreeexponent
C:clusteringcoefficientD:correlationdegree

19. Conclusion&Futurework
19
Aframeworkforfindingasetofnew(i.e.notalreadyseen)Web
serviceoperationsthatcanbeintroducedtoagivencollectionof
Webservices.
Meaningfulnessisdeterminedbasedonthesemanticsimilarityto
existingones,
Networkcharacteristicsareusedforcollectiveevaluationofall
recommendedservices(collectivemeaningfulness)
Futurework:
EmployingWebservicecompositionpractices
Devisingmethodsformeasuringtheadded-valueofsuggestedWeb
servicesusingQoSmetrics

20. Gratefultohaveyourcomments?
SHAHABM@KTH.SE
20
Thanks!