Semantic-Based Query Routing and Heterogeneous Data Integration in Peer-to-Peer Semantic Link Networks

Transcription

1 Semantc-Based Query Routng and Heterogeneous Data Integraton n Peer-to-Peer Semantc Lnk Networks Ha Zhuge 1, Je Lu 1, 2, Lang Feng 1, 2 and Chao He 1, 2 1 Chna Knowledge Grd Research Group, Key Lab of Intellgent Informaton Processng, Insttute of Computng Technology Chnese Academy of Scences, Bejng, , Chna {zhuge@ct.ac.cn} 2 Graduate School of the Chnese Academy of Scences {lj@kg.ct.ac.cn, feng_lang@kg.ct.ac.cn, hc@kg.ct.ac.cn} Abstract. A semantc lnk P2P network specfes and manages semantc relatonshps between peers data schemas. The proposed approach ncludes a tool for constructng and mantanng P2P semantc lnk networks, a semantc-based peer smlarty measurement approach for effcent query routng, and peer schema mappng algorthms for query reformulaton and heterogeneous data ntegraton. The advantages of the proposed approach nclude three aspects: Frst, t uses semantc lnks to enrch relatonshps between peers data schemas. Second, t consders not only node but also structure n measurng the smlarty between schemas so as to effcently and accurately forward queres to relevant peers. Fnally, t deals wth semantc heterogenety, structural heterogenety and data nconsstency to enable peers to exchange and translate heterogeneous nformaton n sngle semantc mage. 1 Introducton The orgnal motvaton for most early P2P systems such as Gnutella and Napster s fle sharng [23, 24]. Peer data management systems (PDMS) provde us wth a flexble archtecture for decentralzed data sharng. Usually, a PDMS conssts of a set of peers, and each peer has an assocated XML schema. Heterogeneous data ntegraton for large-scale P2P networks s a challengng ssue due to the autonomous, scalable, dynamc and heterogeneous data characterstcs of peers. Heterogeneous data management n a PDMS concerns the followng three key ssues: 1. How to autonomously dentfy semantcally relevant peers. 2. How to accurately and effcently route a query requrement ntated by one peer to relevant peers so as to avod network floodng. 3. How to ntegrate heterogeneous data flows returned from dfferent peers so as to provde users and other peers wth a sngle semantc mage data usage mode [20, 21], because P2P systems do not have a global schema lke tradtonal data ntegraton systems [15].

2 Prevous research on P2P computng systems and peer data management systems manly concerns data models for P2P databases, peer clusterng, peer searchng and query routng algorthms, and peer schema medaton mechansm. For example, the P2P-based system PeerDB for dstrbuted data sharng [14], the scalable P2P lookup protocol [17], the local relatonal model for medatng between peers n a PDMS [3], approaches for controllng the dstrbuton of peers to cluster and form super-peer networks [10, 13], the archtecture supportng data coordnaton between peer databases [6], approaches to automatc schema matchng [16], the semantc and algorthmc ssues for mappng data n P2P systems [9], the soluton to acheve semantc agreement n a P2P network [1], the generc schema-matchng prototype Cupd [12], query reformulaton algorthms for XML-based peers [5, 7, 8], and the approach for optmzng query reformulaton n a PDMS [18]. But they are not the total solutons to the above three key ssues. Ths paper ntroduces the noton of P2P semantc lnk network to resolve the frst ssue. Semantc relatonshps between peers data schemas are specfed through semantc lnks [19]. Each peer s encapsulated as a soft-devce (.e., a software servce mechansm [20]) that provdes servces to each other and to other vrtual roles accordng to the content of ther resources and the related confguraton nformaton through XML, SOAP (Smple Object Access Protocol) messages and WSDL (Web Servce Descrpton Language). A software tool has been mplemented to assst users to construct and mantan a nested P2P semantc lnk network. To resolve the second ssue, ths paper proposes an approach for measurng semantc smlarty between peers. It consders not only the semantc smlarty between nodes n peers data schemas, but also semantc smlarty between structures n peer schemas. Upon recevng a query, a peer wll autonomously forward the requrement to relevant peers accordng to the types of the semantc lnks as well as the smlarty between nodes and between structures of peer schemas. To resolve the thrd ssue, ths paper establshes three mappngs: semantc node mappng, semantc clque mappng and semantc path mappng to reformulate a query on source schema over target schemas. We apply technologes of QoP (Qualty of Peers) such as response tme, precson and recall to manage nconsstent data n returned data flows. 2 Approach Overvew A P2P Semantc Lnk Network (P2PSLN) s a drected network, where nodes are peers or P2PSLN, and edges are typed semantc lnks specfyng semantc relatonshps between peers [19]. In a P2PSLN, each peer s an actve and ntellgent soft-devce [20], whch can dynamcally and ntellgently establsh semantc connecton wth each other. The role of a peer can be a server when t provdes data, nformaton and servces, a medator when forwardng query requrements, and a clent when accessng nformaton from other peers. As depcted n Fg. 1, each peer n a P2PSLN has two man modules: a communcaton module and a data management module. Peers communcate wth each other

3 through SOAP messages. Users can query a peer through GUI (Graphcal User Interface) or SSeIQL (Sngle Semantc Image Query Language) an SQL-lke query language desgned for P2PSLN-based peer data management. Fg. 1. An overvew of a P2P semantc lnk network The data management module of each peer s responsble for managng query requrements and returned query results. Upon recevng a query requrement, the data management module performs the followng tasks: 1. Query Processng To analyze query requrement and get query parameters. 2. Query Translaton To translate the query requrement aganst the XML schema of the current peer and check whether t can satsfy the requrement. If not, the re-

4 qurement wll be forwarded to the successors who are lkely to answer the query and to forward the query further. 3. Query Evaluaton To pose the query requrement on the current peer to retreve answers. 4. Peer Selecton To select approxmate successors accordng to the semantc relatonshp and semantc smlarty between the current peer and the selected successors. 5. Query Reformulaton To reformulate a query on the current peer over schemas of ts mmedate successors. 6. Query Forward To autonomously forward the query requrement to the successors hghly smlar to the current peer accordng to the routng polcy and a predefned TTL (Tme_to_Lve) value. Upon recevng query results returned from the successors, the data management module of the peer who ntates the query requrement wll frst analyze the result to detect nconsstent data n the returned data flows. For the successors who are lkely to answer the query but return fewer matchng results, the current peer wll send SOAP messages to nqure whether there are some schema changes, and to update the schema mappng, semantc lnk type and smlarty degree between them. Fnally, the data management module wll combne or jon data matchng query requrement n the returned data flows and provde users or peers wth data from multple sources n a unform vew. 3 P2P Semantc Lnk Network Model 3.1 Semantc Lnk In a P2PSLN, a semantc lnk between two peers s represented as a ponter wth a type (α) drected from one peer (predecessor) to another (successor). A semantc lnk can be one of the followng types: 1. Equal-to Lnk, denoted as P equ P j, states that the semantcs of P s equal to that of P j. The equal-to lnk s reflectve, symmetrc and transtve. 2. Smlar-to Lnk, denoted as P (sm, sd) P j, defnes that the semantcs of P s smlar to that of P j, and sd s the smlarty degree between P and P j. 3. Reference Lnk, denoted as P ref P j, defnes that the semantcs of P refers to that of P j. 4. Implcaton Lnk, denoted as P mp P j, defnes that the semantcs of P mples that of P j. The mplcaton lnk s transtve and can help the reasonng mechansm to fnd new semantc mplcaton relatonshps. 5. Subtype Lnk, denoted as P st P j, defnes that the semantcs of P j s a part of P. The subtype lnk has the transtve characterstc. 6. Sequental Lnk, denoted as P seq P j, defnes that the content of P j s the successor of the content of P.

5 7. Empty Lnk, denoted as P P j, represents that there are no semantc relatonshps between P and P j. 8. Null Lnk or Unknown Lnk, denoted as P N P j, represents that the semantc relatonshps between P and P j are uncertan or unknown. We can chan relevant semantc lnks to obtan uncertan semantc relatons between peers accordng to a set of reasonng rules [19]. The heurstc rules sutable for connectng dfferent types of semantc lnks n a P2PSLN are lsted n Table 1, where α {equ, sm, ref, mp, st, seq,, N} denotes the semantc lnk type between peers. Table 1. Resasonng rules for P2P semantc lnk networks No. Rule 1 Rule 2 Rule 3 Rule 4 Rule 5 Rule 6 Rule 7 Rule 8 Rule 9 Rule 10 Rule 11 Rule 12 Rules P equ P P equ P j P j equ P P equ P j, P j equ P k P equ P k P equ P j, P j α P k P α P k P mp P j, P j mp P k P mp P k P st P j, P j st P k P st P k P mp P j, P j st P k P mp P k P mp P j, P j ref P k P ref P k P st P j, P j mp P k P mp P k P st P j, P j ref P k P ref P k P N P j, P j α P k P N P k P P j, P j α P k P N P k 3.2 Operatons on P2P Semantc Lnk Networks A P2PSLN supports three types of operatons: peer jon, peer departure and peer stablzaton. 1. Peer Jon. When a peer P jons a P2PSLN, t wll frst dentfy the semantc relatonshp between tself and a peer P j n the network, and then take P j as ts mmedate successor by callng functon P.Jon (P2PSLN, P j, α), where α denotes the semantc relatonshp between P and P j. The detal of each functon s lsted n Table 2. The semantc relatonshps between P and other peers n the current P2PSLN could be derved accordng to rules shown n Table 1. To fnd other successors, P wll ask each successor P k of P j by callng functon P. FndSuccessors (P2PSLN, P j, α, P k, β). If P α P j, P j β P k P γ P k satsfes the reasonng rules n Table 1, then P makes P k as ts successor, and calls functon P.FndSuccessors (P2PSLN, P k, γ, P m, δ) teratvely. After establshng the semantc relatonshps between P and ts successors, P calls functon P. SchemaInqury (P2PSLN, P j ) to acqure the XML schemas of each successor P j. The process to measure smlarty degree between peers wth Smlar-to lnk type wll be llustrated n Secton 5.

6 2. Peer Departure. When a peer P leaves a P2PSLN, t may notfy ts predecessors and successors before ts departure. In turn, predecessor P j wll remove P from ts successor lst, delete the semantc lnks between P j and P, and add each successor P k of P as ts own successor provded that: (1) P k P j successor lst, and (2) there s a semantc relatonshp between P j and P k. Smlarly, successor P k wll remove P from ts predecessor lst, delete the correspondng semantc lnks, and add each predecessor P j of P as ts own predecessor f: (1) P j P k s predecessor lst, and (2) there s a semantc relatonshp between P k and P j. 3. Peer Stablzaton. To ensure the up-to-date semantc lnks between peers, each peer P n a P2PSLN runs functon P.Stablzaton (P2PSLN, P j ) perodcally n the background and updates semantc lnk types, predecessor ponters and successor ponters accordngly. If P j (.e., the predecessor or the successor of P ) exsts n the network, t wll notfy P ts exstence and schema change nformaton. If P j does not exst n the current P2PSLN, P wll remove P j from ts predecessor/successor lst and modfy ts neghbor ndex accordngly. When the XML schema of a peer changes, t wll autonomously notfy ts predecessors and successors the new schema through SOAP messages. Table 2. Operatons on P2P semantc lnk networks ID Operaton Functon 1 P.Jon (P2PSLN, P j, α) To take P j as the successor of P and specfy the semantc lnk type between P and P j as α n a P2PSLN. 2 P.FndSuccessors (P2PSLN, P j, α, P k, β) 3 P.SchemaInqury (P2PSLN, P j ) 4 P. Departure (P2PSLN) To leave a P2PSLN. 5 P. Stablzaton (P2PSLN, P j ) To deduce semantc relatonshps between P and P k n a P2PSLN provded that P α P j and P j β P k hold. To acqure XML schema of P j n a certan P2PSLN. To ask for the exstence and schema change from P j n a P2PSLN. 3.3 P2P Semantc Lnk Network Defnton Tool There are two knds of basc elements n a nested P2PSLN: nodes and semantc lnks. A node can be ether a peer or a P2PSLN (.e. a component), whle a semantc lnk denotes the semantc relatonshp and smlarty degree between two peer schemas. We have developed a tool to assst users to construct and mantan a P2PSLN. A graphcal nterface of the defnton tool s shown n Fg.2. Users can defne a P2PSLN by clckng the operaton buttons arranged at the top porton and drawng on the screen. The scalable and nested node herarchy of the current P2PSLN s arranged

7 on the left column. The descrpton for each peer (.e., PeerID, Peer Name, Peer IP, Peer Descrpton) and each semantc lnk (.e., Predecessor, Successor, Semantc Relatonshp, Smlarty Degree) s lsted at the bottom. Operaton Buttons P2P Semantc Lnk Network Scalable Herarchy Peer Descrpton Semantc Lnk Descrpton Fg. 2. An nterface of the proposed P2PSLN defnton tool 4 Peer Schema Mappng Peer schema mappng s to resolve the ssue of the semantc nconsstency between source schemas and target schemas. Upon recevng peer schemas through SOAP messages, a peer wll traverse the schemas recursvely n depth-frst order and extract node and path nformaton from the target, then carry out three types of mappngs: semantc node mappng, semantc clque mappng and semantc path mappng. 4.1 Semantc Node Mappng Semantc Node Mappng s to resolve the semantc nconsstency between nodes by mappng nodes n the source schema nto nodes n target schemas. A peer encapsulates a global dctonary that defnes a set of semantcally related terms (synonymy, abbrevatons, etc.) and the smlarty degree between terms. After acqurng the target peer schemas, the source peer wll automatcally buld mappng and smlarty degree between nodes accordng to the defnton n the global dctonary. The nodes n

8 source schemas and the mappng nodes n target schemas are called Semantc Nodes and Semantc Mappng Nodes when semantc lnks have been establshed between the source and the target. We also provde tools to enable users to manually modfy the semantc node mappngs automatcally generated by the system, and to keep the new mappngs by usng a local dctonary. 4.2 Semantc Clque Mappng A semantc clque represents the semantc structure such as the parent-chld relatonshp and ancestor-descendant relatonshp between a set of closely related semantc nodes. The semantcs of a node n a semantc clque s constraned by semantcs of all nodes on the path from the root to t. Semantc Clque Mappng s to dentfy semantc clques (sub-trees that cover a set of closely related semantc nodes) and map each semantc clque n a source schema nto the target schemas, where the mappng mages are called Semantc Mappng Clques. Semantc mappng nodes n a semantc mappng clque hold the semantc structure that the semantc nodes n a semantc clque hold. To fnd the semantc clques, we frst dvde all the semantc nodes n a source schema nto a set of closely related sets,.e., the semantc node set. The followng algorthm s to dentfy the semantc clque correspondng to each semantc node set. Algorthm SemantcClqueRecognton (T 1, T 2, SN) /* Gven a set of closely related semantc mappng nodes, to fnd semantc clques n sub-tree rooted at T 1 and semantc mappng clques n sub-tree rooted at T 2 */ Input: T 1, T 2, SN={SN 1,, SN n } /* SN={SN 1,, SN n } s a set of closely related semantc nodes, SN s a semantc node;*/ Output: SC={SC 1,, SC k }, SMC={SMC 1,, SMC k } /*Semantc clque set n a source schema and Semantc mappng clque set n target schemas*/ Begn IF (T 1 = =Null) THEN Return True; R 1 =T 1.FrstChld; Temp=True; WHILE (R 1! = NULL) R 2 = Semantc- Mappng-Node (T 2, R 1 ); /* To fnd semantc mappng node of R 1 n T 2 */ IF (R 2 = =Null) THEN Return False; ELSE Temp=Temp And SemantcClqueRecognton (R 1, R 2, SN); IF Temp== False THEN Return False; ELSE Add R 1 To SC; /* add R1 to semantc clque set*/

9 Add R 2 To SMC; /*add R2 to semantc mappng clque set*/ R 1 =T 1.NextChld; END IF; END IF; END Whle; Return Temp; End The Maxmum Semantc Clque s the semantc clque that s not semantcally ncluded by any other semantc clque. The Mnmum Common Sub-tree denoted as MCS (SC 1,, SC p, SN 1,, SN q ) s the sub-tree that covers all the semantc clques (SC 1,, SC p ) and all the dentfed semantc nodes (SN 1,, SN q ) not belongng to any semantc clque n a source schema. The root of the mnmum common sub-tree s called the Nearest Common Predecessor of the nvolved semantc clques and semantc nodes. Algorthms to fnd mnmum common sub-tree are ntroduced n [11]. Besdes the semantc clques automatcally dentfed by algorthm Semantc- ClqueRecognton, we have developed a tool to assst users to defne semantc clques that are requred under certan crcumstances. User nterface to defne semantc clques and semantc mappng clques s depcted n Fg. 3. The left-and-mddle porton dsplays the source schema herarchy and the correspondng graphcal representaton, whle the rght-and-mddle porton corresponds to the target schema. The black nodes n the source schema form a user-defned semantc clque, whle black nodes n the target schema are the correspondng semantc mappng clques. Target Schema Herarchy Source Schema Herarchy Semantc Clque Semantc Mappng Clque Fg. 3. User nterface to defne semantc clques and semantc mappng clques Fg. 4 depcts the schema trees of SIGMOD proceedngs and VLDB proceedngs. The dentfed semantc nodes and semantc mappng nodes are the crcles n the same color. The semantc clque, the maxmum semantc clque and the mnmum common sub-tree are denoted by the dashed close curves as descrbed n the legend.

10 Fg. 4. XML trees conformng to the schemas of proceedngs of ACM SIGMOD and VLDB 4.3 Semantc Path Mappng Semantc Path Mappng s to map each semantc path from the root to the semantc nodes n the source schema nto the paths n target schemas (.e., semantc mappng paths). Let Semantc-Path (N ) be the path from Root (N ) to semantc node N n a source schema, and Semantc-Mappng-Path (N ) be the mappng path of Semantc- Path (N ) n target schemas. The process of semantc path mappng can be descrbed as follows:

11 Algorthm SemantcMappngPath (P,N ) Input: P /*Schema of P */; N /*Semantc Node n P */; Output: Semantc-Mappng-Path (N ); Step 1: For each node on Semantc-Path (N ) Fnd semantc mappng nodes n target schemas accordng to node mappng defnton n global dctonary and local dctonary; Step 2: Connect semantc mappng nodes n target schemas to form an dentfed path; Step 3: IF the dentfed path matches a path SMPath n target schemas THEN Return (SMPath); ELSE Extend the dentfed path by replacng par ent-chld relatons wth ancestordescendant relatons between adjacent nodes; IF SMPath n target schema contans the extended dentfed path; THEN Return (SMPath); END IF. Based on the dea llustrated above, Table 3-5 respectvely show the semantc node mappng, the semantc clque mappng and the semantc path mappng correspondng to schemas n Fg. 4. Table 3 s generated accordng to the defnton n the global dctonary and local dctonary. Table 4 s generated based on the algorthm SemantcClqueRecognton (Secton 4.2). Table 5 s formed accordng to the algorthm SemantcMappngPath llustrated above. Table 3. Semantc node mappng between the schema of SIGMOD proceedngs and schema of VLDB proceedngs Source Semantc Node Target Semantc Mappng Node Smlarty Degree (SD) SIGMOD Ttle VLDB Ttle 1 SIGMOD Author VLDB Author 1 SIGMOD Conference VLDB Ttle 0.5 SIGMOD ConfYear VLDB Year 0.9 SIGMOD SIGMOD VLDB VLDB 0.9 SIGMOD VLDB

12 Table 4. Semantc clque mappng between the schema of SIGMOD proceedngs and the schema of VLDB proceedngs Source Semantc Clque Target Semantc Mappng Clque SD SIGMOD Authors (Author,, Author) VLDB Authors (Author,, Author) 1 SIGMOD Artcles (Ttle, IntPage, VLDB Artcles (Ttle, IntPage, 1 EndPage, Authors (Author,, Author)) EndPage, Authors (Author,, Author)) SIGMOD VLDB Table 5. Semantc path mappng between the schema of SIGMOD proceedngs and the schema of VLDB proceedngs Source Semantc Path Target Semantc Mappng Path SD SIGMOD SIGMOD/Proceedng VLDB VLDB/Proceedngs/Proceedng/Artc 1 s/proceedng /Artcles/Artcle/ Ttle les/ Artcle/Ttle SIGMOD SIGMOD/Proceedng VLDB VLDB/Proceedngs/Proceedng/Ttle 0.7 s/proceedng/confere nce SIGMOD VLDB 5 Semantc-based Peer Smlarty Measurements and Query Routng An effectve query should forward queres only to relevant peers whose schemas are lkely to match the queres. So t s necessary to have an effectve smlarty measurement for qualfyng semantc relatvty between peer schemas. We characterze the smlarty degree between a set of peers accordng to the node smlarty and structure smlarty. The smlarty between semantc nodes focuses on obtanng the semantc nteroperablty among peers, and can be measured by the methods of cycle analyss and functonal dependency analyss as proposed n [1]. The smlarty between semantc structures s to capture the semantc structure such as the parent-chld relatonshp between closely related semantc nodes n a maxmum semantc clque or a mnmum common sub-tree. A peer determnes the destnaton to forward a query accordng to the smlarty between semantc nodes and between semantc structures. To defne the smlarty between semantc structures, we ntroduce the followng notons: Peer (N ) denotes the semantc mappng node for semantc node N. Length (N, N j ) denotes the number of nodes on the path from N to N j. MaxSC (N ) denotes the maxmum semantc clque that semantc node N belongs to. MnCS (N ) denotes the mnmum common sub-tree that N belongs to. Semantc-Node-SD (N, N j ) denotes the smlarty degree between N and N j.

13 The algorthm to measure the structure smlarty between the semantc node N n the source schema and ts semantc mappng node N j n the target schema s as follows: Input: N, N j /* N s a Semantc Leaf Node, and N j =Peer (N ) */ Output: Semantc-Structure-SD (N, N j ) /*Semantc structure smlarty between N and N j */ Step 1:IF N belongs to one of the Maxmum Semantc- Clques THEN T=MaxSC (N ) ELSE T= MnCS (N ) END IF Step 2:Root (N )= T IF Length (N, T)=1 THEN Semantc-Structure-SD (N, N j )= Semantc- Node-SD (N, N j ) ELSE NodeSet={N,, Root (N )} /* Nodes on path from N to Root (N ) */ FV = ( fv N,..., fv ( )) Root N /* semantc structure smlarty feature vector */ 0, Peer( N k ) Semantc Mappng Path( N ) fv N = k Semantc Node SD( N k, Peer( N k )), (1) Otherwse W = ( W N,..., W Root( N ) ) /* weght vector to denote node mportance for node on path from N to Root (N )*/ 1/ 2, N k = N W = (1/ 2) k, k = length( N, N ), and N Root( N ) (2) Nk k k n 1 1 W = (1/ 2) n 1 N, n = length( N, Root( N )), N k = Root( N ) l = 1 l Semantc-Structure-SD (N, N j )=, where W FV W FV W FV = W N fv N W Root( N ) fv Root( N ), and X = X = x x END IF 2 k 2 (3) Let SN={N 1,,N m } be semantc node set of source schema, Semantc - Structure - SD = ( SR N,..., SR N ) be the feature vector for the se- 1 m

14 mantc-structure smlarty of each semantc node calculated accordng to formula (3), and W = ( W N,..., W ) 1 N be the user-defned weght vector representng the mportance of each semantc node. The semantc structure smlarty between two peer m schemas s defned as follows: Semantc-Structure-SD (P, P j )= W Semantc Structure SD W Semantc Structure SD (4) 6 Query Reformulaton and Heterogeneous Data Integraton Upon recevng a query requrement, a peer wll dentfy a set of relevant peers accordng to semantc relatonshps and smlarty degree between peers to answer the query. We dstngush query requrements as follows: 1. A query that could be answered by separate peers. 2. A query that should be answered by jonng data on multple peers. Query reformulaton s to reformulate a peer s query over ts mmedate successors, then over the successors mmedate successors, and so on. Whenever the forwarded query requrement reaches a peer that stores the matchng data, the query wll be posed on that peer. The semantc node mappng, semantc clque mappng and semantc path mappng n Table 3-5 are used for reformulatng a query over target schemas. Wthn a predefned tmeout, the peer ntatng a query wll analyze data flows returned. To solve the problem of data nconsstency, we take nto account the QoP, the user-perceved qualtes such as the number of returned results, response tme, traffc overhead, precson and recall etc. The data returned by peers wth hgher QoP s consdered more relable to solve the problem of data nconsstency. Fnally, the peer ntatng the query wll combne or jon relevant data accordng to the pre-defned data flow and then provde users and peers wth a sngle semantc mage. 7 Experments and Dscusson To llustrate and evaluate the proposed approach, we smulate a small but realstc P2PSLN applcaton. The smulaton envronment conssts of 50 peers. Each peer randomly selects a group of peers as ts neghbors, and the average degree s equal to 6. The metadata of 50,000 papers collected from DBLP XML databases [4] and ACM SIGMOD XML records [2] s dstrbuted over all peers under a unform dstrbuton. XML document sze of each peer vares from 275K to 14, 207K. It s assumed that each peer has the same bandwdth and process ablty. Twenty randomly generated queres are randomly submtted to twenty peers to test the performance of the P2PSLN wth the followng two types of routng mechansms: (1) the Breadth Frst Search (BFS), each peer broadcasts query requrements to all the neghbors; and, (2) the Random Walk Search (RW), each peer forward the receved query requests to a number of randomly selected neghbor. Our evaluaton metrcs are the recall rate (.e.,

15 the fracton of the relevant data whch has been retreved), and the bandwdth consumpton (.e., the number of messages per query). In the frst experment we measure the recall rate of three routng mechansms when the TTL feld of the request message s set to 5. Fg. 5 represents recall rate of the three routng mechansms. On average, the recall rate of BFS, P2PSLN, and RW s 0.58, 0.43, and 0.28 respectvely. The BFS routng polcy acheves the hghest recall rate. Ths s because the BFS broadcasts query requrements to all ts neghbors and t s sure to get the most of the relevant data. The P2PSLN forwards query requrement accordng to the semantc relatonshp and the smlarty degree, so t s possble to get the hgher recall than the RW wthn a predefned TTL value. Fg. 5. Recall rate for 20 queres n BFS, P2PSLN, and RW routng polces (TTL=5) In the second experment we measure the number of messages that the three search mechansms generate to process a query requrement wthn a predefned TTL. Fg. 6 shows that the number of messages generated by BFS s the most (25 on average). The number of messages generated by P2PSLN and RW s 9 and 13 on average respectvely. We are able to reduce the number of messages by 2/3 n P2PSLN when compared to the BFS polcy. In the P2PSLN search mechansm, each peer n the query path determnes the neghbors accordng to the semantc relatonshp between them and then sends the query request to 3 neghbors wth the hghest smlarty degree. Therefore, the number of messages to be forwarded can be reduced obvously.

16 Fg. 6. Number of messages generated by 20 queres n BFS, P2PSLN, and RW routng polces (TTL=5) Expermental results show that the P2PSLN s more effectve and effcent n query routng than the BFS and RW routng polcy n general. The major dfferences between the proposed approach and the prevous work are as follows: 1. The P2PSLN specfes semantc relatonshps between peer schemas. Each peer s encapsulated as an actve and ntellgent soft-devce, whch could autonomously dentfy semantc relatonshps and dynamcally nteract wth each other. 2. The semantc-based peer smlarty measurement for effcent query routng provdes a way to measure the smlarty between a set of closely related nodes n peer schemas. We propose the semantc clque to denote the semantc structure between closely related semantc nodes. 3. The semantc node mappng, semantc clque mappng and semantc path mappng resolve the ssues of semantc heterogenety and structural heterogenety between source schemas and target schemas. The data nconsstency ssue n the returned data flows s resolved based on the qualty of nvolved peers. 8 Conclusons To resolve the ssues of heterogeneous data ntegraton n peer data management, ths paper proposes a P2P semantc lnk network, a semantc-based peer smlarty measurement for query routng, and a peer schema mappng approach for query reformulaton. Results from theoretcal analyss and smulatons show that the proposed approach s effectve. Contrbutons nclude three aspects: 1) propose the notons of P2P

17 semantc lnk network and provde wth a tool for constructng and mantanng a nested P2PSLN; 2) ncorporate the semantc node smlarty and the semantc structure smlarty to measure the smlarty between peers so as to mprove the effectveness and effcency of query routng; and 3) provde users and peers wth data obtaned from multple peers n sngle semantc mage. Experments show that the proposed approach s a promsng approach for peer data management. The proposed approach has been ntegrated nto the Chna E-Scence Knowledge Grd Envronment IMAGINE (Integrated Multdscplnary Autonomous Global Innovaton Networkng Envronment),, whch ams at provdng access to dstrbuted resources (.e., nformaton, knowledge and servces) and speedng up the processes of knowledge generaton, propagaton, fuson and management n cooperatve research [21, 22]. Ongong work focuses on ncorporatng user-defned ntegrty constrants and query reformulaton optmzaton nto the proposed approach. ACKNOWLEDGMENTS The research work was supported by the Natonal Grand Fundamental Research 973 Program of Chna and the Natonal Scence Foundaton. We thank all team members of Chna Knowledge Grd Research Group ( for ther dlgent work and cooperaton. References 1. K. Aberer, P. Cudre-Mauroux, and M. Hauswrth. The Chatty Web: Emergent Semantcs through Gosspng. WWW 2003, Budapest, Hungary, May ACM SIGMOD Xml Verson P. Bernsten et al. Data Management for Peer-to-Peer Computng: A Vson. In ACM SIGMOD WebDB Workshop 2002, Madson, Wsconsn, June DBLP XML Database A. Deutsch and V. Tannen. MARS: A System for Publshng XML from Mxed and Redundant Storage. Proceedngs of the 29th VLDB Conference, Berln, Germany, September F. Gunchgla and I. Zahrayeu. Makng Peer Databases Interact A Vson for an Archtecture Supportng Data Coordnaton. In Proc. of the Conference on Informaton Agents (CIA 2002), Madrd, Span, September A. Halevy et al. Schema Medaton n Peer Data Management Systems. In Proc. of ICDE 2003, Bangalore, Inda, March 2003

18 8. A. Halevy et al. Pazza: Data Management Infrastructure for Semantc Web Applcatons. In Proc. of the Intl. WWW Conf. 2003, Budapest, Hungary, May A. Kementsetsds, M. Arenas, and R. Mller. Mappng Data n Peer-to-Peer Systems: Semantcs and Algorthmc Issues. In Proc. of the ACM SIGMOD Internatonal Conference on Management of Data 2003, San Dego, Calforna, June A. Loser et al. Semantc Overlay Clusters wthn Supper-Peer Networks. Internatonal Workshop on Databases, Informaton Systems, and P2P Computng, Berln, Germany, September S.Y.Lu. A Tree-Matchng Algorthm Based on Node Splttng and Mergng. IEEE Transactons on Pattern Analyss and Machne Intellgence (PAMI) 6 (2) (1984) J. Madhavan, P. Bernsten, and E. Rahm. Generc Schema Matchng wth Cupd. Proceedngs of the 27th VLDB Conference, Roma, Italy, September W. Nejdl et al. Super-Peer-Based Routng and Clusterng Strateges for RDF- Based Peer-To-Peer Networks. WWW2003, Budapest, Hungary, May W.S. Ng et al. PeerDB: A P2P-Based System for Dstrbuted Data Sharng. In Intl. Conf. on Data Engneerng (ICDE) 2003, Bangalore, Inda, March B.Oo, Y. Shu, and K. Tan. DB-Enabled Peers for Managng Dstrbuted Data. 5th Asa-Pacfc Web Conference, APWeb2003, Xan, Chna, Aprl E. Rahm and P. Bernsten. A Survey of Approaches to Automatc Schema Matchng. VLDB Journal 10(4) (2001) I. Stoca et al. Chord: A Scalable Peer-to-Peer Lookup Protocol for Internet Applcatons. IEEE/ACM Transactons on Networkng 11 (2003) I. Tatarnov and A. Halevy. Effcent Query Reformulaton n Peer-Data Management Systems. ACM SIGMOD 2004, Pars, France, June H. Zhuge. Actve E-Document Framework ADF: Model and Tool. Informaton and Management 41 (1) (2003) H. Zhuge. Clusterng Soft-Devces n Semantc Grd. IEEE Computng n Scence and Engneerng 4 (6) (2002) H. Zhuge. Chna s E-Scence Knowledge Grd Envronment. IEEE Intellgent Systems 19 (1) (2004) H.Zhuge, Future Interconnecton Envronment Dream, Prncple, Challenge and Practce, Keynote at The 5th Internatonal Conference on Web-Age Informaton Management, Dalan, Chna, July, Gnutella webste Napster webste.