Constructing Service Semantic Link Network Based on the Probabilistic Graphical Model

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1 Intenatonal Jounal of omputatonal Intellgence ystems, Vol. 5, No. 6 (Novembe, 2012), onstuctng evce emantc Lnk Netwok Based on the Pobablstc Gaphcal odel ANPING ZHAO * ollege of ompute and Infomaton cence, hongqng Nomal Unvesty, ollege Town, hapngba Dstct, hongqng, , hna E-mal: apzhao@cqnu.edu.cn YAN A ollege of ompute and Infomaton cence, hongqng Nomal Unvesty, ollege Town, hapngba Dstct, hongqng, , hna E-mal: csmayan@126.com Abstact Automatc sevces collaboaton calls fo the development of semantcally stuctued sevce netwok to maxmze the utlty of Web sevces. evce emantc Lnk Netwok (-LN) s the semantc model fo effectvely managng Web sevce esouces by the dependency elatonshp between sevces. We povded an effectve method fo constuctng -LN based on the gaphcal stuctue epesentaton of the dependences embedded n a pobablstc model. A akov netwok s an undected gaph whose lnks epesents pobablty dependences. We fst leaned akov netwok stuctue fom Web sevces data, and then tansfomed the undected akov netwok stuctue nto a dected gaph stuctue of -LN based on the same ont pobablty dstbuton. Fnally, expemental esults show the effectveness of the method. Keywods: Web sevce, Pobablstc gaphcal model, -LN, evce elatonshp 1. Intoducton The apd ncease n the amount of Web sevce poduced n ecent yeas on the Web has esulted n a moe sophstcated sevce pocess fo e-ommece, whch nvolvng numeous nteactng busness obects wthn complex dstbuted pocesses. Because of the gowng numbe of Web sevces avalable, Web sevces ae beng made avalable to ncease collaboaton n a dstbuted envonment. Howeve, dscoveng such esouces to facltate automatc sevces collaboaton s a mao bottleneck n ths context. In ode to effectvely collaboate between sevces, Web sevces need to be oganzed and the functonaltes semantcally descbed. Ths fact calls fo the development of automatc methods fo sevce elatonshp dscovey and the dependency stuctue n ode to maxmze the utlty of Web sevces by makng them wdely avalable to the communty. One of the key ams of povdng Web sevce esouces wth semantc descptons s to mpove sevce dscovey fo the pupose of collaboaton. emantcally-descbed sevces can not only be seached, bowsed and dscoveed by usng some quees (fo nstance, va the names o task descptons), but also on the bass of the semantc elatedness of the functonaltes. Theefoe, developng a model to descbe the nheent * oespondng autho. Emal: apzhao@cqnu.edu.cn 1040

2 A. Zhao, Y. a dependences and an appoach to dscove semantc assocated sevce netwok among doman-specfed Web sevces s ctcal fo the automatc sevce collaboaton. We use a semantc model evce emantc Lnk Netwok (-LN) to defne semantc stuctue among Web sevces. emantc Lnk Netwok (LN) s a loosely coupled semantc data model fo managng Web esouces. Its nodes can be any types of esouces. Its edges can be any semantc elatons. A semantc lnk netwok nstance s a dected gaph, denoted as (Resouceet, Lnket), whee s the name of the semantc lnk netwok, Resouceet s a set of esouces, and Lnket s a set of semantc lnks n the fom of R R', whee R, R' Resouceet, and s a semantc facto epesentng a semantc elaton between R and R ' [1]. -LN s a specal nstance of LN whee nodes ae Web sevces and acs ae sevce elatonshp lnks to descbe nheent semantc assocaton among Web sevces. -LN s the undelyng semantc model fo effectvely mplementng automatc Web sevce seach and composton by a elatonshp dependency netwok whch connects sevces wth dffeent types of elatonshps. It allows us to specfy the types of the nheent sevce elatonshp dependences to descbe the behavo of Web sevces and focuses on collaboaton, avalablty and navgaton. When manually assgned annotaton elatonshp tags of elated sevces ae not avalable, we hypothesze that automated appoaches could be used to mpove the - LN dscovey pocess. These nclude buldng netwoks of elated sevces. Fo example, a use can seach fo a Web sevce that coesponds to a patcula nput, output o opeaton pefomed. If, howeve, the eteved sevces do not fulfl the all equements, the use may be nteested n explong elated sevces (fo example, wth moe genec/specfc nput/output, but stll wth a elated functonalty), whch can be dentfed by bowsng a Web sevce netwok -LN. Howeve, -LN dscovey s always hghlghted n eal applcatons, both on pecse sevce seach and on automatc sevces collaboaton. The manly advantages of constuctng -LN ae: () Adng n analyss. The semantcally stuctued netwok of sevce makes sevce ease to fnd and collaboaton. Identfyng sevce semantc lnk netwok would automate pat of the analyss pocess. () Reducng netwok complexty. As the sze of Web sevce gows on the web, t becomes nceasngly dffcult to navgate o dsplay the sevce space. Abstactng semantcally stuctued sevce netwok can educe the sze and complexty of the netwok epesentaton, makng t moe manageable wth exstng navgaton and vsualzaton technques. Ths pape focuses on how to dscove -LN and dscove dependency elatonshps among Web sevces by combnng seveal dependency epesentaton methods of gaph ncludng undected gaph stuctue akov netwok and dected gaph stuctue. We popose a methodology to dscove sevce semantc lnk netwoks, whch can help to dentfy elated sevce esouces on the bass of the nheent sevce elatonshp dependences. We ae nteested n a patcula poblem whee the nput s sevce datasets wth the hstocal nvocatons and output s an -LN, namely dscoveng -LN fom a lage amount of Web sevces. The contbuton of ths pape s as follows: () We ncopoate LN to constuct evce emantc Lnk Netwok -LN based on the dependency elatonshp between sevces. () Development of a gaphcal model based appoach fo automated dscovey of -LN fom sevce dataset. () We conduct pelmnay expements and pefomance studes, whch vefy the feasblty and effectveness of ou methods. The est of ths pape s oganzed as follows: In secton 2 we evew elated wok, ecton 3 ntoduces the poblem defnton and pelmnaes elated to ou wok. ecton 4 descbes the appoach to -LN constucton based on akov stuctue. We show the effectveness of the pesented appoach by expemental esult n secton 5. Fnally, we summaze ou conclusons and the futue eseach n secton Related Wok ost of the effots n the doman of emantc Web fo Web sevce have been focused on sevce elatonshp automatc dscovey fo sevce collaboaton (e.g. automatc Web sevce seach and composton), usng both manual and automated appoaches. 1041

3 evce emantc Lnk Netwok Lu et al. A sevce dscovey method was poposed fo the collaboaton based on ollaboated emantc Lnk Netwok [2]. The netwok s constucted usng sevces epesented by Element Fuzzy ogntve aps (E- Fs) and the smla and assocated elatons between E-Fs as well. An automatc semantc elatonshp dscoveng appoach fo constuctng the semantc lnk netwok was pesented [3]. The basc pemse s that the semantcs of a web page can be eflected by a set of keywods, and the semantc elatonshp between two web pages can be detemned by the semantc elatonshp between the keywod sets. The appoach adopts the data mnng algothms to dscove the semantc elatonshps between keywod sets. Dong et al. [4], fo example, used clusteng-based appoach n whch paametes of sevce opeatons ae gouped nto meanngful concepts, whch ae then used to fnd smla sevce opeatons based on smla paametes. Howeve, ths method povdes only a lmted soluton and s unable to povde compehensve sevce dscovey based on the undelyng semantcs povded by sevces. Employng emantc Web appoaches such as ontologcal annotatons could mpove ths appoach [5]. Recently, these effots have been extended to semantcally stuctued netwok descpton and dscovey of esouces that ae used to analyze, vsualze and exploe such a netwok fo potentally mpovng the esouce dscovey pocess. By studyng the ntnsc elatonshp between semantc communtes and the semantc space of LN, appoaches to dscoveng easonng-constant, ule-constant, and classfcaton-constant semantc communtes ae poposed [6]. Appoaches make use of the semantc communtes and the emegng semantc elatons n a dynamc complex netwok of leanng esouces to suppot effectve leanng. Z. Huang and Y. Qu popose a taton emantc Lnk Netwok (-LN) to descbe the semantc nfomaton ove the lteatue ctaton netwoks [7]. A famewok of the constucton of - LN s epesented by ntegatng seveal NLP methods. The methods of aggegatng a -LN and the algothms of dscoveng opnon communtes n a - LN ae dscussed. ult-pespectve exploaton on the -LN can effectvely fnd atcles of hgh mpotance, aggegate the functon of ctatons and detect opnon communtes among scentfc documents. The appoach of mnng ecung stuctues that epesents an nteestng aea of the semantc web was pesented [8]. The ecung stuctues could be mned by exstng o novel knowledge dscovey methods. The pape [9] apples automated text mnng technques to text-based communcaton to dentfy, descbe and evaluate undelyng socal netwoks among onlne communty membes fo automate dscoveng socal tes that fom communty between membes and pesents a contentbased method fo automated dscovey of socal netwoks fom theaded dscussons, efeed to as name netwok. The name netwok method can be used to study onlne classes and to look fo evdence of collaboatve leanng n onlne leanng communtes. Thee have been a numbe of ecent effots to mnng semantc netwoks n Bonfomatcs e-resouces fom the lteatue [10]. These effots mostly ely on manual ceaton and ae unable to cope wth the huge nflux of vaous electonc esouces, whch consequently esult n the unavalablty to the communty. They pesent a text mnng appoach that utlzes the lteatue to extact and semantcally pofle bonfomatcs esouces. Resouces ae then ethe clusteed o lnked nto a netwok, povdng the uses wth a possblty to exploe tools, sevces and datasets based on the elatedness, thus potentally mpovng the esouce dscovey pocess. A mnng soco-semantc netwoks method fo egocentc and polycentc quees n multdmensonal netwoks s poposed [11]. The method allows fast seach fo obects n suffcent poxmty of othe obect(s) whee the poxmty s defned n tems of multple elatonshps between obects. The advantages of such an appoach ae hgh pefomance and hgh scalablty n tems of sze of multdmensonal netwok. Authos pesent an appoach to mnng a semantc netwok of Bookmaks fo Web each and Recommendaton based on a semantc smlaty measue fo URLs that takes advantage both of the heachcal stuctue of the bookmak fles of ndvdual uses, and of collaboatve flteng acoss uses [12]. Even though the applcaton aea s dffeent n ths wok, some of the deas ae bult on a common gound. As compaed wth ou wok, a methodology to dscove -LN, whch can help to dentfy elated sevce esouces on the bass of the nheent sevce 1042

4 A. Zhao, Y. a elatonshp dependences embedded n pobablstc models, s to be dscussed. 3. Poblem Defnton and Pelmnaes Gaphcal models (akov netwoks) ae an mpotant subclass of statstcal models that possess advantages that nclude clea semantcs and a sound and wdely accepted theoetcal foundaton (pobablty theoy). Gaphcal models can be used to epesent effcently the ont pobablty dstbuton of a doman. They have been used n numeous applcaton domans, angng fom dscoveng gene expesson pathways n bonfomatcs to compute vson. In eal-wold sevce paadgms, the nheent semantc assocaton dependences between Web sevces can be dscoveed by gatheng and mnng the dstbuted hstocal nvocatons mpled by OAP messages [13]. Natually, statstcal analyss s one of the wdely adopted appoaches; one poblem that natually ases s the constucton of a sevce elatonshp netwok based on the theoy of gaphcal models. In ou poblem, we beleve that the stuctue of -LN epesents the condtonal ndependence elatonshps among the Web sevces. -LN s dected acyclc gaph n natue n whch the nodes epesent Web sevces (vaables), the lnks (acs) sgnfy the exstence of dect causal nfluences between the lnked vaables, and the stengths of these nfluences ae expessed by fowad condtonal pobabltes. The stuctue of a akov netwok gaphcally encodes a set of condtonal ndependences among the vaables n the doman. The akov stuctue s adopted as the backbone famewok of -LN. Ths s a bass of ou followng dscusson. Knowledge of these ndependences s nvaluable n a numbe of felds, especally those that ely moe on pobablty dstbutons. A soluton to ths poblem s to lean an undected gaph stuctue based on pobablstc dependences fom sevce data, then to tansfom the undected gaph stuctue nto a dected gaph stuctue -LN. Theefoe, we seek effectve gaphcal epesentaton of the dependences embedded n a pobablstc model. A akov netwok s an undected gaph whose lnks epesents pobablty dependences, whle -LN s a dected gaph whose dected lnks epesent condtonal pobablty dependences. Leanng akov netwok s ease than leanng -LN because t doesn t need to fnd the decton of an edge. In ths pape, we employ a akov netwok to descbe the sevce elatonshp dependences between sevces based on the ont pobablty dstbuton. Fst, we lean the gaphcal stuctue of Web sevce. By mnng hstocal nvocatons among Web sevces, we adopt a dependency analyss based akov netwok leanng appoach to dscoveng the stuctue of akov netwoks. Then, we tansfom found akov netwok nto a dected gaph stuctue wth the same ont pobablty, at the same tme, the decton of lnks n dected gaph ust shows the semantc nteacton elatonshp between Web sevces. o afte fndng a akov netwok and annotatng semantc lnks, an equvalent -LN can be obtaned by epesentng the same ont pobablty. As stated above, we adopt the pobablty dependency appoach to constuct the stuctue of -LN so that the pobablty based -LN can be the semantc model of epesentng the dependency elatonshp between Web sevces. Defnton 1 -LN A sevce semantc lnk netwok -LN s a dected gaph, denoted as s (evceet, Lnket), whee s s the name of the -LN, evceet s a set of Web sevces, and Lnket s a set of semantc lnks n fom of ', whee, ' evceet, and s a semantc facto epesentng a semantc elaton between and '. Defnton 2 akov Netwok Let U {,, } be a fnte set of vaables n the doman. A akov netwok s an undected gaphcal model whch epesents the ont pobablty dstbuton oveu. A akov netwok of a doman s an undected gaph model that can be used () to epesent the ont pobablty dstbuton of the doman, and () to epesent the set of condtonal ndependences pesent n the doman. Each node n the gaph epesents one of the andom vaables n the doman, whle the absence of edges encodes condtonal ndependences among them. Followng, we ntoduce some basc defntons of gaph model and akov netwok [15], whch ae the bass of ou late dscusson. 1043

5 evce emantc Lnk Netwok Defnton 3 ondtonally Independence Let U {,, } be a fnte set of vaables wth dscete values, let ) be a ont pobablty functon ove the vaables nu, and let X,Y and Z stand fo any thee dsont subsets of vaables n U. X andy ae sad to be condtonally ndependence gven Z,denote as I ( X, Z, Y ) ff x y, z) x z), Especally, f Z, I( X,, Y ) ff x y) x) wheneve P ( y) 0. Defnton 4 Dependency odel A dependency model s the set of tples ( X, Z, Y ) such that I ( X, Z, Y ) s tue, denote as I ( X, Z, Y ). haactezng a gven dependency model by an undected gaph G ( V, E) s desed, we have ndependence dependences and condtonal ndependences by a gaphcal epesentaton of a dependency model. We mean a dect coespondence between the elements nu (of ) and the set of vetces n V (of G ), such that the topology of G eflectng some popetes of. When ths coespondence s establshed, we wll make no dstncton between U and V but wll wte G ( V, E). Let X, Y and Z ae thee dsont subsets of V, f Z ntecepts all paths between the nodes of X and those of Y, denoted as X Z Y G, then ths ntecepton should coespond to condtonal ndependence between X and Y gven Z, namely, X Z Y G I ( X, Z, Y ), and convesely, I ( X, Z, Y ) X Z Y G. But thee s often no way of usng vetex sepaaton n G to dsplay all dependences and condtonal ndependences. Theefoe, the defnton s gven fo weake condton. Defnton 5 D-map; I- map; P-map An undected gaph G s a dependency map (D-map) of f thee s a one-to-one coespondence between the elements of U and the nodes V of G, such that fo all dsont subsets X,Y, Z of elements we have I ( X, Z, Y ) X Z Y G. mlaly, G s an ndependency map (I-map) of f I ( X, Z, Y ) X Z Y G. G s a pefect map (P-map) of f t s a D-map and I-map, I( X, Z, Y ) X Z Y G. A D-map guaantees that vetces found to be connected ae ndeed dependent n. It may, howeve, dsplay a pa of dependent vaables as a pa of sepaated vetces. An I-map, convesely, guaantees that vetces found to be sepaated coespond to ndependent vaables but does not guaantee that all those shown to be connected ae n fact dependent. Defnton 6 nmal I-map A gaph G s a mnmal I-map, f deletng any edge of G would make G cease to be an I-map. uch a gaph s called a akov netwok of. Defnton 7 akov Bounday A akov blanket BL I ( ) of an element U s any subset of elements fo whch I(,, U a) and The set s called a akov bounday of, denoted as B ( ), f t s a mnmal akov blanket of. mlaly, the akov bounday of n G, denoted as B G ( ). Defnton 8 hodal Gaph An undected gaph G ( V, E) s sad to be chodal f evey cycle of length fou o moe has a chod (an edge onng two non-consecutve vetces). Defnton 9 Decomposable Relatve A pobablty model P s sad to be decomposable f t has a mnmal I-map that s chodal. P s sad to be decomposable elatve to a gaph G f the followng two condtons ae met: () G s I-map of P. () G s chodal 4. evce emantc Lnk Netwok onstucton 4.1. evce akov Netwok In ths subsecton we addess the poblem of leanng the stuctue of the sevce akov netwok fom data. akov netwok s gaphcal statstcal models whose stuctue can be epesented by an undected gaph. A akov netwok conssts of two pats: an undected gaph (the model stuctue), and a set of paametes. An example akov netwok s shown n Fg.1, fo a doman contanng eght vaables. Leanng such c a d Fg. 1. An example akov netwok g b h e f 1044

6 A. Zhao, Y. a models fom data conssts of two ntedependent poblems: leanng the stuctue of the netwok, and, gven the leaned stuctue, leanng the paametes. In ths wok we focus on stuctue leanng of the akov netwok fom Web sevce data, whch s fequently the most challengng of the two tasks. As stated n ecton 3, n ode to constuct -LN fom Web sevces, we wll fst lean a sevce akov netwok stuctue descbed the nheent dependency elatonshps between sevces. In ou wok, akov stuctue s adopted as the backbone famewok of - LN fo ts effectve epesentaton sevce elatonshp. In the followng, we gve some defntons of the semantc model fo Web sevces. Defnton 10 evce luste A sevce cluste can be defned as follows: { 1, 2,, n}, 1 n Whee 1,, epesent odeed sevces n a gven doman. n ( 1 n ) s a sepaate sevce epesented as an opeaton n the coespondng WDL document. Defnton 11 evce nvocaton lnks A sevce nvocaton lnk l ( ID, 1, 2,, n ), 1 n, Whee ID s thea sevce nvocaton lnkevce nvocaton lnk, ( 1, 2,, n ) epesents an nvocaton tace of Web sevces n a equest executon. Defnton 12 Relatonshp atx of evce Invocaton The elatonshp matx of sevce nvocaton s a matx a11 a12 a1 n a21 a22 a2n am 1 am2 amn uch that: Each ow n coesponds to a Web sevce of ; each column n coesponds to a sevce nvocaton lnk l ; and the element a 1 f s nvoked n a l, a 0 othewse. Hstocal sevce nvocatons ae necessay to dscove the nheent semantc assocaton dependences among Web sevces. Unde the basc achtectue of Web sevces, hstocal sevce nvocatons can be ecoded by pasng the coespondng OAP messages [13]. And <OAP-ENV:Envelope Xmlns:OAP-ENV=" envelope/" OAP-ENV:encodngtyle=" <OAP-ENV:Heade> <H> <paent_h></paent_h> <message_d> </message_d> <message_name>getzpcode</message_name> <souce>zpcode.com</souce> <taget>offceplace.com</taget> </H> </OAP-ENV:Heade> <OAP-ENV:Body> <GetPostoffceByty> <ty xs:type= xsd:stng >Beng</ty> </GetPostoffceByty> </OAP-ENV:Body> </OAP-ENV:Envelope> Fg. 2. A sample of OAP message fagment wth H n ou pevous wok [14], we use a semantc soap heade H fo gatheng necessay semantc nteacton nfomaton between Web sevces, whch s a collecton of management attbutes that s padded to evey message flowng between Web sevces. The OAP message wth H s shown as Fg. 2. Fom the OAP message, we can see the sevce nvocaton elatonshp between sevces. Fom the defnton 6, we can know that a mnmal I- map can epesent the most of dependency elatonshp n the dependency model, so we dese to dscove a sevce akov netwok whch s a mnmal I-map. Evey dependency model satsfyng symmety, decomposton, and ntesecton has a unque mnmal I- map G0 ( U, E0 ) poduced by connectng only those pas (, ) fo whch I (, U, ) s FALE. (, ) E0 ff I (, U, ) [15].Ths theoem guaantees that f the gaph meets the condton I (, U, ), then the gaph s a mnmal I-map. We use the exstng Edge-deletng algothm [16] whch based on the above theoem to fnd akov boundaes accodng to the gven hstocal nvocatons of sevces, and futhe obtan the akov netwok of sevces. lealy, ths akov netwok s a mnmal I-map and epesents most of the dependences of sevces n sample data. Dung the pocess of akov netwok dscovey, we know that the most challengng and tme-consumng s how to pefom tests of condtonal ndependences. That s, we need to lean the akov netwok stuctue by dentfyng the condtonal ndependence elatonshps among the nodes. In ou wok, we take advantage of an mpotant concluson n nfomaton 1045

7 evce emantc Lnk Netwok theoy condtonal mutual nfomaton [17] to test condtonal ndependency. The mutual nfomaton between vaables A and B measues the expected nfomaton ganed about B, afte obsevng the value of the vaable A. Whle n akov netwok, f two nodes ae dependent, knowng the value of the one node wll gve us some nfomaton about the value of the othe node. Hence, the mutual nfomaton between two nodes can tell us f the two nodes ae dependent and f so, how close the elatonshp s. We use followng equaton to test I ( X, Z, Y ) : x z y Z Y X x, y z) I( X, Y Z) x, y, z) log,( X, Y, Z) x z) y z) Whee s sevce cluste, P ( x, y, z) s magnal pobablty, P ( x, y z), P ( x z), P ( y z) ae condtonal pobablty. agnal pobablty and condtonal pobablty can be obtaned by computng the coespondng fequences fo sample data (the matx of sevce nvocatons) espectvely. (1) N x, y, z x, y, z) (2) N whee N x, y, z s the numbe of columns n matx whch x, y, z ae nonzeo. N s the total numbe of columns n matx. x, y, z) P ( x, y z) (3) z 1) mlaly, we can compute x z) and P ( y z). x, z) x z) (4) z 1) P ( y, z ) P ( y z ) (5) P ( z 1) Gven theshold value, f I( X, Y Z), then I ( X, Z, Y ) holds Dected lque Tee Ou fnal goal s to fnd what s connected to what that s, whch nodes should be oned by dected lnks to epesent the dependency nvocaton elatonshp between Web sevces. The semantcs of -LN depends on ts netwok stuctue and semantc elatonshp ove the stuctue. Thus, the semantcs of - LN demands a clea coespondence between the topology of a dected gaph and the dependence elatonshps potayed by t. Ths suggests that the dected -LN stuctue also can be leaned by dentfyng the condtonal ndependence elatonshps among the sevces. Wth akov netwoks ths coespondence was based on a smple sepaaton cteon: If the emoval of some subset Z of nodes fom the netwok endeed nodes X and Y dsconnected, then X and Y wee poclamed to be ndependent gven Z. Dected gaph use a slghtly moe complex sepaablty cteon, called d-sepaaton, whch takes nto consdeaton the dectonalty of the aows n the gaph. A path between nodes X and Y s closed, gven some evdence, f X and Y ae condtonally ndependent gven. Based on ths concept, all the vald condtonal ndependence elatons n a dected gaph -LN pobablty dstbuton can be dectly deved fom the topology of the coespondng -LN. We ae to tansfom undected gaph nto a dected gaph based on the same ont pobablty dstbuton. That s, the ont pobablty dstbuton epesentaton of akov netwok s epesented by the poduct fom of condtonal pobabltes of dected gaph. We can see that the dected gaph epesentaton captues a lage set of pobablstc ndependences. Fom the chan ule of basc pobablty theoy, we know that evey dstbuton functon can be epesented as a poduct P x,, x ) x ) x x ) x x,, ) (6) ( 1 n n 1 xn 1 Based on ths, we let the ght pat of the condtonal pobablty be the paents and the left be the chlden, and the semantc lnks (dected edges) ust epesent some type of elatonshp between sevces,.e.. o a dected gaph -LN wth condtonal dependency can be dscoveed. Fo example, gven an odeed sevce set ( 1, 2,, n ) accodng to the nvocaton elatonshp, f we expand P n the ode dctated by the sevce lnk, and use the condtonal ndependences encoded n the chan, we obtan 1046

8 A. Zhao, Y. a P ( 1 1,, n ) P ( 1 ) P ( 2 1 ) P ( n n ) (7) Thee s a way to fnd poduct fom of ont pobablty dstbuton of akov netwok by poduct dvson. Ths method fo expessng the ont dstbuton s to dvde the poduct of the magnal dstbutons on the edges (clques) by the poduct of the dstbutons of the ntemedate nodes (the ntesectons of the clques). Theoem 1 Jont dstbuton of the chodal akov netwok G can be wtten as a poduct of the dstbuton of the clques of G dvded by a poduct of the dstbutons of the ntesectons. Poof: Let G be the dscoveed sevce akov netwok. G s I-map and chodal, we obtan the tee T of the clques of G, and the odeed clques { c1, c2,, c, } that s consstent wth T. Fo evey we have a unque pedecessos ( ) such that c () s adacent to c n T. Namely, c () sepaates c fom { c1, c2,, c, } n any such odeng. Based on the chan ule of pobablty theoy, ont dstbuton of G can be epesented as P x, x,, x ) = P ( c c1,, c 1 ) ( 1 2 n = P ) ( c c ( ) = P c c c ) ( ( ) c ) = c c ( ) Based on the theoem 1, we use a Gaph Tangulaton algothm [15] to obtan the clques { c1, c2,, c, } of sevce akov netwok, thus, the ont dstbuton of the sevce akov netwok can be epesented as a poduct of the condtonal pobablty as equaton (6). Whee P ( c c ( ) ) ( c1 c2 1 ) (8) c ) c ) (9) Let c1 { x1, x2,, x}, the P ( c 1 ) can be futhe epesented as poduct of condtonal pobablty based on the chan ule of pobablty theoy, c1) x1) x2 x1) x x1,, x 1) (10) At ths pont, we tansfom the ont pobablty of dscoveed akov netwok nto an equvalent poduct of condtonal pobablty. Theefoe, the ont pobablty P of dscoveed akov netwok s expessble n tems of a poduct of condtonal pobablty. As a esult, we get a poduct of edge pobabltes. The only equement s that as we ode the nodes fom left to ght, evey node except the fst should have at least one of ts gaph neghbos to ts left. The sevce akov netwok can be tansfomed nto a dected clque tee emantc Lnks Annotaton The dected clque tee tansfomed fom the sevce akov netwok s a dected gaph, whee ts node s a set of Web sevces, and dected edge between Web sevces epesents some knds of sevce dependency elatonshp. But the specfc type of sevce semantc elatonshp s not explct, we need annotate semantc lnks to edge of dected clque tee such that t can epesent specfc sevce semantc elatonshps as a semantc lnk. A sevce semantc lnk s an odeed elatonshp between two Web sevces. It can be epesented as a ponte wth a type dected fom one sevce (pedecesso) to anothe sevce (successo). Types of semantc lnks can be defned accodng to the specfc Web sevce applcaton doman. x types of emantc evce Lnks ae defned as follows: () mlato lnk If two sevce and ae smla n semantcs, then sm thee s a mlato lnk between and, denoted as sm. () Refeence lnk A hghe-ganulaty sevce { 1, 2,, n}, n whch s a Web sevce that conssts of moe than one dffeent component sevces, 1 n, then ef thee s a Refeence lnk between and, denoted as ef. () Invocaton lnk If the pedecesso sevce nvocate the successo sevce n a sevce composton pocess, then thee nv s a Invocaton lnk between and, denoted as nv. (v) EqualTo lnk 1047

9 evce emantc Lnk Netwok If two sevce and ae equal n semantcs, then equ thee s a EqualTo lnk between and, denoted as equ. (v) OthogonalWth lnk If two sevce and ae ndependent of each othe n semantcs, then thee s an OthogonalWth ot lnk between and, denoted as ot. (v) embehp lnk If two sevce and belong to the same class of sevces, then thee s a embehp mem lnk between and, denoted as mem. We use a sevce elatonshp mnng method [18] fo obtanng specfc sevce semantc lnks among Web sevce. And a sevce semantc elatonshp matx can be used to annotate semantc lnks n a dect clque tee. Defnton 13 evce emantc atx The sevce semantc matx of n Web sevces,,, can be epesented as: 1 2 n s ( ) nn n n2 1n 2n nn satsfyng: () {equ } ; () If and thee exst sm, ef, nv, mem, then { sm, ef, nv, mem } ; () Othewse, { ot } ; whee epesents the specfc semantc elatonshp between sevce and. The semantc elaton fom a esouce to tself can be egaded as equ, so fo any Web sevce, equ. If thee does not exst dependent semantc lnks between two sevces and, ot. Fo a gven -LN, the coespondng sevce semantc matx s unque and vce vesa. Assume a consstent - LN conssts of n Web sevces, we can deve the elable semantc elatons of any two sevces fom a sevce semantc matx of n sevces. Annotatng sevce semantc lnks n a dected clque tee s to add the semantc facto on the dected edges between two Web sevces. Theefoe, we annotate semantc lnks to dected clque tee by scannng sevce semantc matx and then we can obtan a fnal sevce semantc lnk netwok -LN. As stated above, the man steps of -LN constucton based on pobablstc dependency of sevces can be summazed as follows: () A completely connected gaph G fo all Web sevces s constucted. Based on the condtonal mutual nfomaton fom hstocal nvocatons as sample data, some edges wll be emoved fom G such that G s a sevce akov netwok and epesents most of the dependences of sevces n sample data. () Tangulatng the obtaned sevce akov netwok G fo epesentng the ont dstbuton of as the poduct fom of condtonal G pobabltes. () Tansfomng G nto a dected clque tee based on the same ont pobablty dstbuton, and annotatng semantc lnks n dected clque tee to obtan the fnal sevce semantc lnk netwok - LN. Based on the above steps, the whole algothm fo - LN constucton s descbed as follows. Algothm: on-ln Input: sample dataset of sevces Output: an -LN 1. onstuct a complete connected gaph G 0 fo Web sevces n ; 2. Fo each edge, n G 0 3. If I (,, ) Then delete, ; 4. End Fo 5. Tangulate G0 by Gaph Tangulaton algothm; 6. Obtan odeed clques 1, 2, m ; 7. Reode sevces n as 1, 2, n ; 8. Tansfom the ont dstbuton of G 0 nto the poduct fom of condtonal pobabltes; 9. k 1; 10. Whle ( ) 11. Fo k to n Do 12. Let be a oot of -LN; 13. Assgn a magnal pobablty P ( 1,, n ) to ; 14. Fo 1to n Do 1048

10 A. Zhao, Y. a 15. If s dependent on Then annotatng semantc lnks ; 16. End Fo 17. End Fo 18. = { }; 19. k k 1; 20. Retun an -LN Unvesal Descpton, Dscovey, and Integaton (UDDI) egstes, seach engnes, and sevce potals. We manually ceated a total of 200 sevce nvocaton lnks by human expeences fom QW Dataset. The pat of the esultng -LN s shown n Fg 3. It s well known that the most tme-consumng of the algothm s condtonal ndependences tests. We manly focus on analyss the tme complexty of condtonal ndependences test. As fo a complete connected undected gaph G 0, thee n( n 1) n( n 1) ae edges. We only need to do tmes 2 2 condtonal ndependences test, and the tme complexty of condtonal ndependences test depends on sze of ont pobablty table of sample Web sevces data. If thee ae N tples of the ont pobablty table 2 of sample data, the tme complexty of tests s O ( N ). o as fo the whole algothm, the tme complexty s exponental, the effcency of the algothm also depends on the sze of sample data. 5. Expemental Results In ths secton, we wll pesent the pelmnay expements to test the pefomance of ou poposed - LN constucton appoaches. What ou manly focus s that whethe the esultng - LN could epesent the tue dependency elatonshp between sevces accuately. o the accuacy of the esultng -LN s used to assess the qualty of the outcome netwok. Because of the atfcal data set has the dsadvantage of a andom netwok topology. Real data on the othe hand, may come fom non-andom topologes and allow a moe ealstc assessment of the pefomance. Thus, assessment of the accuacy of the esultng -LN makes moe sense fo eal-wold data sets. We conducted expements usng a publcly avalable test set QW Dataset Veson 2.0 [20, 21, 22]. The man goal of QW Dataset s to offe a bass fo Web evce eseaches. The updated QW Dataset Veson 2.0 ncludes a set of 2507 eal Web sevces that exst on the Web today. The maoty of Web sevces was obtaned fom publc souces on the Web ncludng Fg. 3. The pat of the esultng -LN When we geneated the test data, a elatonshp matx of sevce nvocaton and a cetan pobablty of nvocatons among gven sevces was defned. Thoughout the expements, we tested the accuacy to evaluate the qualty of the dscoveed -LN by seachng the composte Web sevce whch composed by seveal assocated component sevces. And the composte sevce s pedefned by expet expeences. The accuacy s defned as Rs acc (11) Q s Whee Qs s the numbe of assocated component sevces of equested composte Web sevce Qs contans, Rs s the numbe of etuned sevce set R s based on esultng -LN whch ae also appea n the Q s. We seached 10 composte Web sevces fo testng the accuacy of esultng -LN, and the numbe of component sevce that the composte sevce 1049

11 evce emantc Lnk Netwok contans fom at least of 5 to at most of 10. The expemental esults ae shown n Fg. 4. Accuacy Fg It can be seen that the accuacy value s hgh and bascally stable. Ths means that the dscoveed -LN s much effectve by ou poposed method. These expemental esults vefy the effectveness of the dscoveed -LN. As a esult, the -LN s an effectve semantc model fo epesentng the tue dependency elatonshp between Web sevces. Geneally, above expemental esults and analyss show ou poposed method fo constuctng the -LN fom sample Web sevce data s effcent, feasble and pactcal. 6. onclusons omposte sevce Accuacy on sevce seachng by the - LN In ths pape we poposed and exploed a gaphcal model based methodology fo constuctng a sevce semantc lnk netwok of semantcally and functonally elated Web sevces. The man motvaton s to facltate the sevce dscovey and automatc collaboaton pocess, whch would mpove the avalablty and utlty of these sevces to the communty. We have pesented a smple defnton of semantcally stuctued netwok -LN based on LN and dscussed an appoach of -LN constucton that ae used to analyze, and exploe such a netwok fo potentally mpovng the sevce dscovey pocess and sevce collaboaton. The methodology evolves aound the dependency elatonshp among sevces and the topology dependency stuctue of -LN n natue, whch ae the detemnatve aspects of the semantcs of -LN. The wok pesented hee demonstates the potental of smple gaphcal method that constuctng -LN based on akov netwok. Whle because of the complex nteelatonshps between Web sevces, the stuctue of -LN wll dynamcally vay fom tme to tme. A sevce should on and leave an -LN feely. Ths ssue s at least woth nvestgatng and wll be pat of ou futue wok. Ou analyss has led us to advocate dscoveng fo semantcally stuctued netwok as a futful aea of eseach, the poblems should be amenable to staghtfowad knowledge dscovey methods. Futhemoe, even unsophstcated technques could be benefcal, as elatvely some exstng esults stll offe some assstance whee thee cuently s none. Acknowledgements Ths wok was patally suppoted by the Doctoal Reseach Fund of hongqng Nomal Unvesty (No.11XWB021) and the Humanty and ocal cence Foundaton fo Youth cholas of nsty of Educaton of hna (No.11YJ ). Refeences 1. H. Zhuge, Y. un, The chema Theoy fo emantc Lnk Netwok, Futue Geneaton ompute ystems.26 (3) (2010) F. Lu et al, Dscovey of Web sevces based on ollaboated emantc Lnk Netwok, n Poc. IEEE Intenatonal Wokshop on emantc omputng and ystems H. Zhuge et al, An automatc semantc elatonshp dscovey appoach, n Poc. WWW 2004, (2004) p X. Dong et al, mlaty seach fo Web sevces, In Poc. The Thteth ntenatonal confeence on Vey lage data bases, (2004) pp Wolstencoft et al, The mygd Ontology: Bonfomatcs evce Dscovey, Intenatonal Jounal of Bonfomatcs Reseach and Applcatons, 3(2007)pp H. Zhuge, ommuntes and Emegng emantcs n emantc Lnk Netwok: Dscovey and Leanng, IEEE Tansactons on Knowledge and Data Engneeng. 21 (6) (2009) Z. Huang and Y. Qu, A multple-pespectve appoach to constuctng and aggegatng taton emantc Lnk Netwok, Futue Geneaton ompute ystems. 26 (3) (2009) R. Wolfe and R.. Kelle, Explotng Recung tuctue n a emantc Netwok, In Poc. Wokshop on 1050

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