Ontology Mapping: As a Binary Classification Problem

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1 Fourth Internatonal Conference on Semantcs, Knowledge and Grd Ontology Mappng: As a Bnary Classfcaton Problem Mng Mao SAP Research mng.mao@sap.com Yefe Peng Yahoo! ypeng@yahoo-nc.com Mchael Sprng U. of Pttsburgh sprng@ptt.edu Abstract Ontology mappng seeks to fnd semantc correspondences between smlar elements of dfferent ontologes. Ontology mappng s crtcal to acheve semantc nteroperablty n the WWW. To solve the ontology mappng problem, ths paper proposes a non-nstance learnng-based approach that transforms the ontology mappng problem to a bnary classfcaton problem and utlzes machne learnng technques as a soluton. Same as other machne learnng based approaches, a number of features (.e., lngustc, structural and web features) are generated for each mappng canddate. However, n contrast to other learnng-based mappng approaches, the features proposed n our approach are generc and do not rely on the exstence and suffcency of nstances. Therefore our approach can be generalzed to dfferent domans wthout extra tranng efforts. To evaluate our approach, two experments (.e., wthn-task vs. cross-task) are mplemented and the SVM algorthm s appled. Expermental results show that our non-nstance learnng-based ontology mappng approach performs well on most of OAEI benchmark tests when tranng and testng on the same mappng task; and the results of approach vary accordng to the lkelhood of tranng data and testng data when tranng and testng on dfferent mappng tasks.. Introducton The World Wde Web (WWW) s wdely used as a unversal medum for nformaton exchange. However, semantc nteroperablty n the WWW s stll lmted due to the heterogenety of nformaton. Ontology, a formal, explct specfcaton of a shared conceptualzaton [5], has been suggested as a way to solve the problem. Wth the popularty of ontologes, ontology mappng that ams to fnd semantc correspondences between smlar elements of dfferent ontologes has attracted many research attentons from varous domans. Dfferent technques have been examned n ontology mappng, e.g., usng lngustc technques to measure the lexcal smlarty of concepts n ontologes [3], treatng ontologes as structural graphs [0], takng the advantage of nformaton retreval technques [9], applyng heurstc rules to look for specfc mappng patterns [6], and learnng to map ontologes through machne learnng technques [2][3]. Comprehensve surveys of ontology mappng approaches can be found n [4][8]. Prevous learnng-based approaches have acheved hgh accuracy n predcton of correct mappngs n the cases reported n [2][3]. However the approaches ether have a lmtaton that t heavly reles on the avalablty of nstance data when measurng the smlarty of classes/attrbutes, or requre new tranng data to rebuld ther model when doman changes and thus restrct the unversalty of the model. To overcome the lmtatons, we treat the ontology mappng problem as a bnary classfcaton problem. We learn a generc mappng model, whch does not requre the exstence of nstances and doman constrants. To learn a model, a varety of features that can reflect the characterstcs of mappng pars are generated, and then the SVM algorthm s appled. Expermental results show that our nonnstance learnng-based ontology mappng approach performs well n most of OAEI benchmark tests when tranng and testng on the same mappng task; and the results of approach vary accordng to the lkelhood of tranng data and testng data when tranng and testng on dfferent mappng tasks. 2. Problem Statement Ontology s a formal, explct specfcaton of a shared conceptualzaton n terms of classes, attrbutes and relatons [5]. Ontologes are typcally represented as taxonomc trees that nclude classes, propertes, and relatons, and assocated wth nstances. Two sample bblographc ontologes are /08 $ IEEE DOI 0.09/SKG

2 shown n Fg., n whch the ellpses ndcate classes (e.g., "Reference"), the dashed rectangles ndcate propertes (e.g., "publsher"), the lnes wth arrowhead ndcate "subclassof" relaton between two classes, and the sold rectangles ndcate nstances of class (e.g., "Obect-orented data modelng"). Each class and property can also have descrptve nformaton (e.g., ID, label, comment) and restrctons (e.g., ttle, publsher) as ndcated n the brace next to "Book". Proceedng Monograph Collecton -press -event -edtor -organzaton Reference ID: Book Label: Book Comments: A book subclassof that may be a monograph or a collecton of wrtten Book texts. Restrctons: ttle, publsher, author, edton, date etc. subclassof subclassof Fg.. Two sample bblographc ontologes The process of ontology mappng s to fnd semantc correspondences between smlar elements n two homogeneous ontologes, and many ways can be used to udge the qualty of a mappng result. In ths paper, we refer to the "correspondence" as an "=" relatonshp, the "elements" as "classes" and "propertes" of an ontology, and we udge the mappng result by ts correctness,.e., ether correct or ncorrect, whch can be depcted as a bnary set {+,-}. Therefore the ontology mappng problem can be easly transformed as a bnary classfcaton problem represented as followng statement: m( e, e2, r) {, where e s element e from ontology O, e 2 s element e from ontology O 2, and r s the mappng relaton (.e. correspondence) between e and e 2. Accordng to the statement, canddate mappngs n Fg. can be evaluated as followngs: m(book rght, Book left, =) {+}, m(proceedngs, Proc, =) {+}, m(monograph, Monography, =) {+}, m(proceedngs, Talks, =) {-}, m(proceedngs, Monography, =) {-}, etc. 3. Our Approach 3. Overvew -Obect-Orented Data Modelng -Publshed by MIT Press -Year 2000 Composte subclassof Monography Collecton The nsght of our approach s to treat ontology mappng problem as a bnary classfcaton problem, Proc. -publsher -edtor -organzaton subclassof Book } ID: Book Label: Book Comments: A book that may be a monography or a collecton of wrtten texts. Restrctons: humancreator, edton and thus we can take advantage of machne learnng technques. Generally speakng, our approach has 5 steps, see detaled descrpton n 4.4. and Generate varous doman ndependent features (.e., lngustc, structural and web features) to descrbe the characterstcs of ontologes. 2. Randomly generate tranng and testng set for OAEI benchmark tests. 3. Tran a SVM model on tranng set. 4. Classfy testng data on the traned SVM model. 5. Extract mappng results of testng data usng naïve descendant extracton algorthm []. 6. Evaluate testng data aganst ground truth. 7. Fnally, repeat step tmes and get the average evaluaton result to elmnate bas. 3.2 Feature Generaton Applyng machne learnng technques to ontology mappng context rases the queston of what types of nformaton should be used n the learnng process. Many dfferent types of nformaton can contrbute toward decdng the correspondence of a mappng par. Two prncples are followed to select features: The feature should not be lmted to nstances. It could be generated from classes, propertes and/or nstances n ontologes. The feature should be general enough and doman ndependent so that the model could be generalzed to other applcatons regardless of the varety of doman. In the approach, 3 categores,.e., lngustc features, structural features and web features, and total 26 features are generated for each mappng par Lngustc Features Lngustc features are selected accordng to the prncple descrbed n [7]. Totally 6 lngustc features are generated, whch can be dvded nto two types (We do not lst all lngustc features due to the space lmt): Isolated characterstcs of elements n mappng par, e.g. length of elements, number of tokens, etc. 2 Syntactc characterstcs of mappng par, e.g. (normalzed) length dfference between elements, Levensthten edt dstance between two elements, the proporton of word change between elements, number of common tokens n the par, the cosne smlarty of the profle [9][0] of elements, etc. 2

3 3.2.2 Web Features Bollegala, Matsuo et al. [] proposed a page count based co-occurrence measure.e., WebDce, to compute semantc smlarty, whch s defned as followng, where the notaton H(X) and H(Y) denote the page counts for query X and Y respectvely n a search engne, H(XY) denotes the page counts for the conuncton query X AND Y, c s a predefned threshold (e.g. c=5) to reduce the adverse effects caused by random co-occurrences. 0 WebDce( X, Y) 2H ( X Y ) H ( X ) H ( Y) f H(X Y) c otherwse Structural Features Structural nformaton s mportant n estmatng the smlarty of ontologes. Table lsts the structural features of a mappng canddate. 4. Evaluatons 4. Test Ontologes Our test ontologes are OAEI benchmark tests ontologes, orgnatng from the bblography doman. The OAEI benchmark tests nclude one reference ontology O R dedcated to the very narrow doman of bblography, multple test ontologes O T manually dscardng varous nformaton from the reference ontology n order to evaluate how algorthms behave when nformaton s lackng, and 4 real world bblographc ontologes that are generated by MIT 2, UMBC 3, Unversty of Karlsruhe 4 and INRIA 5 respectvely. The OAEI benchmark tests are open tests, whch mean the expected results are provded for all partcpants. 4.2 Evaluaton Crtera We follow the evaluaton crtera used by the OAEI ontology matchng campagn That s, standard nformaton retreval evaluaton measures,.e., precson, recall and f-measure, are computed aganst the reference algnment. The precson, recall and f- measure are defned as follows lpes.exmo.rdf.bb.owl Precson Recall F-measure # correct _ found _ mappngs p # all _ found _ mappngs # correct _ found _ mappngs r # all _ possble_ mappngs p r f 2 p r 4.3 Expermental Desgn Motvaton Two experments were desgned. The motvaton of them s: The st experment nvestgates how the approach performs n the stuaton where people have manually marked some mappng results for a specfc mappng task, but they need help from automatc mappng tools to fnd the rest of mappngs. The 2 nd experment nvestgates whether a model traned on one mappng task can work on another mappng task(s). Moreover, we are nterested n whch benchmark test(s) s more sutable as a tranng model. The motvaton for the 2 nd experment s: n most ontology mappng cases, no ground truth s avalable for a specfc mappng task, but a general model has been learned that can be used to fnd mappngs. Thus, to save users tme and effort, we want to fnd out mappng results usng the exstng model. 4.4 Expermental Methodology and Results 4.4. st Experment Wthn-task The methodology of the st experment s:. For each OAEI benchmark test, we generate canddate mappng pars by smply combne all elements from two ontologes. 2. For each mappng canddate, we mark down ther correctness accordng to the reference algnment (.e. the ground truth). Smultaneously we generate varous features (.e., lngustc, structural and web features) to descrbe the characterstcs of the mappng par. 3. We splt all mappng pars nto two groups (.e., one s for tranng purpose and the other s used as testng set) by randomly choosng (e.g. 50% vs. 50%). We tran two SVM models (.e., SVM- Class and SVM-Property) on tranng set usng SVM-Lght package

4 Table. Structural features Elements Features Descrpton DrPropNumDff The normalzed dfference between the numbers of the classes drect propertes The edt dstance based smlarty between the classes drect propertes,.e., DrPropSm DrPropSm Avg (max( EdtDstSm( p, p2 ))), where p and p 2 are drect propertes of class C and C 2. chnumdff The normalzed dfference between the numbers of the classes subclasses. The edt dstance based smlarty between the classes subclasses,.e., Classes chsm chsm Avg (max( EdtDstSm( subc, subc2 ))), where subc and subc 2 are subclasses of class C and C 2. The edt dstance based smlarty between the classes super classes,.e., pasm pasm Avg (max( EdtDstSm( pac, pac2 ))), where pac and pac 2 are super classes of class C and C 2. depdff The normalzed dfference between the depth to root of the classes domansm The edt dstance based smlarty between the propertes doman Propertes rangesm The edt dstance based smlarty between the propertes range mothersm The edt dstance based smlarty between the propertes mother class 4. We classfy testng data on two models.. On Test #0-#04 and #22-#247, both SVM- 5. We extract mappng results of testng data usng naïve descendant extracton algorthm [] and evaluate the results aganst reference algnment. Class model and SVM-Property model perform as well as PRIOR+. Ths s because the lngustc nformaton of these test ontologes s hghly 6. Fnally to elmnate the bas caused by randomly choosng mappng pars to generate tranng and testng data n step 3, we repeat step tmes and report the average result as our fnal result. In the experment, two SVM models (.e., SVMsmlar wth that of the reference ontology and there s much less nterference such as randomly generated name of classes/propertes. Thus t s easy for both SVM-Class and SVM-Property model to catch useful features lke edt dstance Class model for classes and SVM-Property model for that can contrbute to learnng models. propertes) are traned separately due to the dfference between the structure of classes and propertes. As a result, the mappng pars of classes are tested on SVM-Class model and the mappng pars of propertes are tested on SVM-Property model. Moreover, snce the number of negatve examples s much larger than the number of postve examples n tranng data, we use a fxed cost factor 2. On Test #20-#20, both SVM-Class and SVM- Property model perform relatvely worse than the PRIOR+ (especally on #20, #202, #208, #209). Ths s because the lngustc nformaton changes too much on these tests so that t s hard to catch ts lngustc and web characterstcs n the tranng model. Meanwhle the structural feature s relatvely weak. (.e. 0) n SVM-Lght to equalze the dstrbuton and ensure tranng errors on postve examples outwegh those on negatve examples. Fg. 2 shows the average f-measure of classes of each OAEI benchmark task tested on SVM-Class model. Fg. 3 shows the f-measure of propertes of each OAEI benchmark task tested on SVM-Property model, n whch the f-measures of benchmark tests 3. On Test #248-#266, both SVM-Class and SVM- Property model perform much worse than the PRIOR+. Ths s because there s no name and no comments n the test ontologes at all,.e., both lngustc features and web features are totally unavalable. The only feature avalable for SVM models s structural, whch s relatvely weak. Meanwhle, the PRIOR+ benefts from the #226, #233-#237, #240-#247, #250, #254-#257, profle enrchment process that ntegrates #260-#266 are 0 s because there s no property exstng for those tests. For comparson purpose both nstance nformaton, whch keeps all descrptve nformaton, to both classes and propertes. Fg 2 and 3 nclude the f-measure of 4. On real world cases #30-304, the SVM-Class classes/propertes runnng by PRIOR+ approach [0], a non learnng based ontology mappng approach. The observatons from Fg. 2 and 3 are: model performs much better than the PRIOR+ and the SVM-Property model performs smlarly as the PRIOR+ (.e., slghtly better on #30 and #302 but slghtly worse on #303 and #304). The reason s our learnng based approach utlzes 23

5 SVM-Class Pror+ Class SVM-Property Pror+ Property F-Measure F-Measure Fg. 2. Results of classes on SVM-Class model on all benchmark tests (Wthn-task) Web feature to explore synonymous relatons between concepts n ontologes. By contrast the PRIOR+ approach does not ntegrate any auxlary thesaurus for such a purpose. Our concluson s: For learnng-based approach (wthn-task), the performance s good when mappng task s relatvely easy (.e., #xx and #22-247). When mappng task s more dffcult, ts performance s not as good as the PRIOR+ approach (.e., #20- #20 and #248-#266). But the performance of ths approach s better than the PRIOR+ on real world cases, whch shows the features used n ths approach make more sense on real world cases than on artfcally constructed cases nd Experment Cross-task The methodology of the 2 nd experment s:. Same as step n st experment. 2. Same as step 2 n st experment. 3. We tran two SVM models (.e. SVM-Class and SVM-Property for each benchmark mappng task, except #228, #233, #236, #239-#247, #250, #254, #257, and #260-#266, usng SVM-Lght package. Ths s because no propertes exst n these test ontologes, and thus no SVM-Property model can be traned on them. And thus t does not make sense to test mappng tasks wth both classes and propertes on the model traned wthout property. 4. We classfy testng data of all the other benchmark tests (excludng the one that has been used n tranng model) usng the SVM models. 5. We extract mappng results of usng naïve descendant extracton algorthm and evaluate the results aganst the reference algnment. 6. Fnally we repeat step tmes and report the average f-measure of a group of testng data (e.g., #xx, #2xx, #3xx etc.) on each tranng model as our fnal result. Fg. 4 shows the average f-measure tested on dfferent data sets (.e., all tests, #xx, #2xx, #3xx, Fg. 3. Results of propertes on SVM-Property model on all benchmark tests (Wthn-task) and more specfc #20-#20, #22-#238, #248- #259). Our concluson s: For learnng-based crosstask approach, the performance s good when tranng data and testng data share smlar characterstcs. If the testng mappng task s very smple, t's easy to catch characterstcs n the tranng model and thus get good performance wth more dffcult tranng task. Meanwhle f both tranng and testng tasks are dffcult but wth dfferent characterstcs, the performance s not as good as other approaches. 5. Related Work Dfferent approaches have been explored to solve ontology mappng problem, among whch machne learnng based method s effcent when the concepts n ontologes are assocated wth many nstances, and t works better f many value of nstances are text rather than references to other nstances. In GLUE [2], a well-known machne learnng based ontology mappng system, to measure the smlarty of concepts the author needs to calculate the ont probablty dstrbuton of the concepts that heavly rely on the avalablty of nstance. However, n most cases nstances are ust unavalable or nsuffcent, and t s more common to have references between nstances than text descrpton. Furthermore, the target of the GLUE s every element n the target ontology, whch makes the model unable to be generalzed to any applcaton where doman has changed. Therefore they need new tranng data to rebuld the model for each doman, whch s usually unavalable. Another approach usng machne learnng technques for ontology mappng s QOM [3]. In QOM, the authors frst calculate varous smlartes based on expert encoded rules, and then they use neural network to ntegrate all these smlarty measures. In the contrast, the features we use are not lmted to the varety of smlartes

6 .2 xx xx 3xx all 0.8 F-Measure Conclusons and Future Work In ths paper, we examned a non-nstance learnngbased ontology mappng approach, whch overcomes the lmtatons of prevous learnng-based ontology mappng approaches that ether rely on the avalablty of suffcent nstances or are domandependent. In the approach we treated the ontology mappng problem as a bnary classfcaton problem; generated a number of generc features; utlzed these features to buld tranng model; and conducted two experments to nvestgate the performance of machne learnng technques n dfferent stuatons. The experment results show that our approach performs well on most of OAEI benchmark tests when tranng and testng on the same mappng task; and the results of approach vary accordng to the lkelhood of tranng data and testng data when tranng and testng on dfferent mappng tasks. Future work may nclude: Leverage dfferent features so as to acheve a robust semantc smlarty measure, do feature selecton procedure by maxmzng the f-measure, and perform an actve learnng wth Support Vector Machne algorthm. 7. References. Bollegala, D., Matsuo, Y. and Ishzuka, M. (2007) Measurng Semantc Smlarty between Words Usng Web Search Engnes. Proceedngs of the Internatonal World Wde Web Conference, Banff, Canada Doan, A., J. Madhaven, et al. (2003). "Learnng to Match Ontologes on the Semantc Web." VLDB Journal 2(4): Ehrg, M. and S. Staab. QOM: Quck Ontology Mappng. In the Proceedngs of the 3rd Internatonal Semantc Web Conference (ISWC) pror+ Tranng Set Fg. 4. Testng results of benchmark tests (Cross-task) 4. Euzenat, J., Bach, T., et al. State of the art on ontology algnment, Knowledge web NoE Gruber, T. "A Translaton Approach to Portable Ontology Specfcatons." Knowledge Acquston 5(2): Hovy, E. Combnng and standardzng large-scale, practcal ontologes for machne translaton and other uses. In Proceedngs of the st Internatonal Conference on Language Resources and Evaluaton (LREC), Granada, Span R. Jones, B. Rey et. al. Generatng Query Substtutons. In WWW '06: Proceedngs of the 5th nternatonal conference on World Wde Web. New York, NY, USA, Kalfoglou, Y. and M. Schorlemmer "Ontology mappng: the state of the art." Knowledge Engneerng Revew 8():-3 9. Mao, M. Ontology Mappng: An Informaton Retreval and Interactve Actvaton Network Based Approach. In Proceedng of ISWC 2007, LNCS 4825, pp , Mao, M. and Peng, Y. PRIOR+ System: Results for OAEI In Proceedngs of ISWC 2007 Ontology Matchng Workshop Melcke, C. and Stuckenschmdt, H. Analyzng Mappng Extracton Approaches. In Proceedngs of ISWC 2007 Ontology Matchng Workshop. Busan, Korea. 2. Melnk, S., H. Garca-Molna, et al. Smlarty floodng: a versatle graph matchng algorthm and ts applcaton to schema matchng. Proc. 8th Internatonal Conference on Data Engneerng (ICDE) Qu, Y., Hu, W., and Cheng, G. Constructng vrtual documents for ontology matchng. In Proceedngs of the 5th Internatonal Conference on World Wde Web