Aligning Anatomy Ontologies in the Ontology Alignment Evaluation Initiative

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1 Aligig Aatmy Otlgies i the Otlgy Aligmet Evaluati Iitiative Patrick Lambrix, Qiag Liu, He Ta Departmet f Cmputer ad Ifrmati Sciece Liköpigs uiversitet Liköpig, Swede Abstract I recet years may tlgies have bee develped ad may f these tlgies ctai verlappig ifrmati. T be able t use multiple tlgies they have t be aliged. I this paper we preset ad discuss results frm aligig tlgies i a real case, the aatmy case i the 2008 Otlgy Aligmet Evaluati Iitiative. We d this by briefly describig a base system fr tlgy aligmet, SAMBO, ad a extesi, SAMBOdtf, ad preset ad discuss their results fr the aatmy case. SAMBO ad SAMBOdtf perfrmed best ad secd best amg the 9 participatig systems. SAMBO uses a cmbiati f strig matchig ad the use f dmai kwledge. SAMBOdtf uses the same strategies but, i additi, uses a advaced filterig techique that augmets recall while maitaiig a high precisi. Further, we describe the first results tlgy aligmet usig a partial referece aligmet. 1 1 Itrducti I recet years may tlgies have bee develped. Ituitively, tlgies (e.g. [6]) ca be see as defiig the basic terms ad relatis f a dmai f iterest, as well as the rules fr cmbiig these terms ad relatis. They are csidered t be a imprtat techlgy fr the Sematic Web. Otlgies are used fr cmmuicati betwee peple ad rgaizatis by prvidig a cmm termilgy ver a dmai. 1 This paper is partly a revised ad updated versi f the paper [12] fcusig the aatmy tlgy aligmet task, ad partly a exteded versi. The frmer paper ctais brief descriptis f the systems, but, fllwig the traditi f the Otlgy Aligmet Evaluati Iitiative, was writte befre the fial results were available. They prvide the basis fr iterperability betwee systems. They ca be used fr makig the ctet i ifrmati surces explicit ad serve as a idex t a repsitry f ifrmati. Further, they ca be used as a basis fr itegrati f ifrmati surces ad as a query mdel fr ifrmati surces. They als supprt clearly separatig dmai kwledge frm applicatibased kwledge as well as validati f data surces. The beefits f usig tlgies iclude reuse, sharig ad prtability f kwledge acrss platfrms, ad imprved dcumetati, maiteace, ad reliability (e.g. [7]). Otlgies lead t a better uderstadig f a field ad t mre effective ad efficiet hadlig f ifrmati i that field. May f the curretly develped tlgies ctai verlappig ifrmati. Fr istace, Ope Bimedical Otlgies (OBO, lists 26 differet aatmy tlgies (Jauary 2009). Ofte we wuld wat t be able t use multiple tlgies. Fr istace, cmpaies may wat t use cmmuity stadard tlgies ad use them tgether with cmpay-specific tlgies. Applicatis may eed t use tlgies frm differet areas r frm differet views e area. Otlgy builders may wat t use already existig tlgies as the basis fr the creati f ew tlgies by extedig the existig tlgies r by cmbiig kwledge frm differet smaller tlgies. I each f these cases it is imprtat t kw the relatiships betwee the terms i the differet tlgies. Further, the data i differet data surces i the same dmai may have bee atated with differet but similar tlgies. Kwledge f the itertlgy relatiships wuld i this case lead t imprvemets i search, itegrati ad aalysis f data. It has bee realized that this is a majr issue ad sme rgaizatis have started t deal with it. 13

2 t l g i e s istace crpus Preprcessig geeral dictiary matcher matcher matcher cmbiati filter mappig suggestis accepted ad rejected suggestis user dmai thesaurus cflict checker Figure 1: Aligmet framewrk [11, 10]. I the remaider f this paper we say that we alig tw tlgies whe we defie the mappig relatiships betwee terms i the differet tlgies. We discuss results frm aligig tlgies i a real case, the aatmy case i the 2008 Otlgy Aligmet Evaluati Iitiative, e f the best kw bechmark cases. We d this by presetig the tw state-f-the-art tlgy aligmet systems that perfrmed best ad secd best, ad preset ad discuss their results fr the aatmy case. 2 Backgrud 2.1 Framewrk A large umber f tlgy aligmet systems have bee develped. Fr a verview f mst f these systems, we refer t review papers (e.g. [11, 16, 15, 8]), the tlgy matchig bk [4], ad the tlgy matchig web site at May tlgy aligmet systems are based the cmputati f similarity values betwee terms i differet tlgies ad ca be described as istatiatis f the geeral framewrk defied i [11, 10] (figure 1). The framewrk csists f tw parts. The first part (I i figure 1) cmputes mappig suggestis. The secd part (II) iteracts with the user t decide the fial mappigs. I II a l i g m e t A aligmet system receives as iput tw surce tlgies. The tlgies ca be preprcessed, fr istace, t select pieces f the tlgies that are likely t ctai matchig terms. The aligmet algrithm icludes e r several matchers, which calculate similarity values betwee the terms frm the differet surce tlgies ad ca be based kwledge abut the liguistic elemets, structure, cstraits ad istaces f the tlgy. Als auxiliary ifrmati ca be used. Mappig suggestis are the determied by cmbiig ad filterig the results geerated by e r mre matchers. By usig differet matchers ad cmbiig ad filterig the results i differet ways we btai differet aligmet strategies. The suggestis are the preseted t the user wh accepts r rejects them. The acceptace ad rejecti f a suggesti may ifluece further suggestis. Further, a cflict checker is used t avid cflicts itrduced by the mappigs. The utput f the tlgy aligmet system is a aligmet which is a set f mappigs betwee terms frm the surce tlgies. 2.2 SAMBO ad SAMBOdtf SAMBO ad SAMBOdtf are based the framewrk described i secti 2.1. They d t have a preprcessig step. SAMBO ad SAMBOdtf ctai curretly five basic matchers [11]: tw termilgical matchers (a basic matcher ad a extesi usig WrdNet; extesi described belw), a structure-based matcher (which uses the is-a ad part-f hierarchies f the surce tlgies), a matcher based dmai kwledge (described belw), ad a learig matcher (which uses life sciece literature that is related t the ccepts i the tlgies t defie a similarity value betwee the ccepts). I additi t these techiques we have als experimeted with ther matchers [13, 18, 21]. The user is give the chice t emply e r several matchers durig the aligmet prcess. We have tw strategies t cmbie the results frm differet matchers. Oe strategy is t give weights t the differet matchers ad the similarity values are the cmputed as a weighted sum f the similarity values cmputed by the differet matchers. The ther strategy defies the similarity f a pair f terms as the maximum value f the similarity values fr the pair cmputed by the differet matchers. The filterig methd i SAMBO is sigle threshld filterig. Pairs f terms with a similarity value higher tha r equal t a give threshld value are retured as 14

3 mappig suggestis t the user. Figure 3: Mappig suggesti. Figure 2: Cmbiati ad filterig. Figure 2 shws a screesht frm the SAMBO system with the five matchers, a weighted sum cmbiati ad the sigle threshld filterig. SAMBOdtf implemets the duble threshld filterig methd develped i [2]. The duble threshld filterig apprach uses the structure f the tlgies. It is based the bservati that (fr the differet appraches i the evaluati i [11]) fr sigle threshld filterig the precisi f the results decreases ad the recall icreases whe the threshld decreases. Therefre, we prpse t use tw threshlds. Pairs with similarity value equal t r higher tha the upper threshld are retaied as suggestis. The ituiti is that this gives suggestis with a high precisi. Further, pairs with similarity values betwee the lwer ad the upper threshld are filtered usig structural ifrmati ad the rest is discarded. We require that the pairs with similarity values betwee the tw threshlds are reasable frm a structural pit f view. 2 The ituiti here is that the recall is augmeted by addig ew suggestis, while at the same time the precisi stays high because ly structurally reasable suggestis are added. The duble threshld filterig apprach ctais the fllwig three steps. (i) Fid a csistet suggesti grup frm the pairs with similarity value equal t r higher tha the upper threshld. We say that a set f suggestis is a csistet suggesti grup if each ccept ccurs at mst ce as first argumet i a pair, at mst ce as secd argumet i a pair ad fr each pair f suggestis (A,A ) ad (B,B ) where A ad B are ccepts i the first tlgy ad A ad B are ccepts i the secd tlgy: A BiffA B. (ii) Use the csistet suggesti grup t partiti the rigial tlgies. (iii) Filter the pairs with similarity values betwee the lwer ad upper threshlds usig the partitis. Oly pairs f which the elemets belg 2 I ur implemetati we have fcused the is-a relati. t crrespdig pieces i the partitis are retaied as suggestis. Fr details we refer t [2]. Based the results frm the matchers, cmbiati ad filterig algrithms, mappig suggestis are prvided t the user. Figure 3 shws such a suggesti. SAMBO displays ifrmati (defiiti/idetifier, syyms, relatis) abut the surce tlgy terms i the suggesti. Fr each mappig suggesti the user ca decide whether the terms are equivalet, whether there is a is-a relati betwee the terms, r whether the suggesti shuld be rejected. If the user decides that the terms are equivalet, a ew ame fr the term ca be give as well. Up a acti f the user, the suggesti list is updated. If the user rejects a suggesti where tw differet terms have the same ame, she is required t reame at least e f the terms. The user ca als add cmmets a mappig relatiship. At each pit i time durig the aligmet prcess the user ca view the tlgies represeted i trees with the ifrmati which actis have bee perfrmed, ad she ca check hw may suggestis still eed t be prcessed. A similar list ca be btaied t view the previusly accepted mappig suggestis. I additi t the suggesti mde, the system als has a maual mde i which the user ca view the tlgies ad maually map terms. 2.3 Otlgy Aligmet Evaluati Iitiative - Aatmy case The Otlgy Aligmet Evaluati Iitiative (OAEI, is a yearly iitiative that was started i The gals are, amg thers, t assess the stregths ad weakesses f aligmet systems, t cmpare differet techiques ad t imprve evaluati techiques. This is t be achieved thrugh ctrlled experimetal evaluati. Fr this purpse OAEI publishes differet cases f tlgy aligmet prblems, sme f which are pe (referece aligmet 15

4 is kw befrehad), but mst are blid (referece aligmet is t kw - participats sed their mappig suggestis t rgaizers wh evaluate the perfrmace). WrdNet UMLS I the aatmy case (versi 2008) participats are required t alig the Adult Muse Aatmy (2744 ccepts) ad the NCI Thesaurus - aatmy (3304 ccepts). The case is divided it 4 tasks (f which task 4 was ew fr 2008). The aatmy case is a blid case. The referece aligmet (the crrect sluti accrdig t the rgaizers) ctais 1523 equivalece mappigs f which 934 are deemed trivial (i.e. they ca be fud by a relatively basic strig-based matcher). Oly equivalece crrespdeces betwee ccepts are csidered. t l g i e s Preprcessig TermWN cmbiati filter mappig suggestis UMLSKSearch a l i g m e t I all tasks the tw tlgies shuld be aliged. The results f the experimets are give i terms f the quality f the mappig suggestis. The evaluati measures are precisi, recall, recall+ ad f-measure. Precisi measures hw may f the mappig suggestis were crrect. It is defied as the umber f crrect suggestis divided by the umber f suggestis. Recall measures hw may f the crrect mappigs are fud by the aligmet algrithm. It is defied as the umber f crrect suggestis divided by the umber f crrect mappigs. Recall+ is the recall cmputed with respect t -trivial mappigs. F-measure is the weighted harmic mea f precisi ad recall. I task 1 the system shuld be tued t ptimize the f-measure. This meas that bth precisi ad recall are imprtat. The systems are cmpared with respect t precisi, recall, f-measure ad recall+. Fr the f-measure i task 1, precisi ad recall are evely weighted. Nie systems participated i this task. I tasks 2 ad 3, i which fur systems participated, the system shuld be ptimized with respect t precisi ad recall, respectively. The f-measure is cmputed with a uevely weighted precisi ad recall (factr 5). I task 4, i which fur systems participated, a partial referece aligmet is give which ca be used durig the cmputati f mappig suggestis. It ctais all trivial ad 54 -trivial mappigs i the referece aligmet. I this case precisi, recall ad f-measure are cmputed with respect t the -give part f the referece aligmet. Figure 4: SAMBO ad SAMBOdtf fr OAEI. 3 SAMBO ad SAMBOdtf fr OAEI The OAEI ly evaluates the -iteractive part f the tlgy aligmet systems. Therefre, we used a variat f the systems withut the user iterface (see figure 4). Further, it wuld t make sese t have mappig suggestis where a ccept appears mre tha ce as the user wuld t be able t make a chice. Therefre, we decided t filter ur systems mappig suggesti lists such that ly suggestis are retaied where the similarity betwee the ccepts i the mappig suggesti is higher tha r equal t the similarity f these ccepts t ay ther ccept accrdig t the mappig suggesti list. (I the case there are differet pssibilities, e is radmly chse. I the implemetati the first i the list is chse.) Fr the OAEI we used the fllwig matchers. The matcher TermWN ctais matchig algrithms based the textual descriptis (ames ad syyms) f ccepts ad relatis. I the curret implemetati, the matcher icludes tw apprximate strig matchig algrithms (-gram ad edit distace), ad a liguistic algrithm that als uses WrdNet ( t fid syyms ad is-a relatis. Our matcher UMLSKSearch uses the Metathesaurus i the Uified Medical Laguage System (UMLS, The similarity f tw terms i the surce tlgies is 16

5 determied by their relatiship i UMLS. I ur experimets we used the UMLS Kwledge Surce Server t query the UMLS Metathesaurus with surce tlgy terms. The queryig is based searchig the rmalized strig idex ad rmalized wrd idex prvided by the UMLS Kwledge Surce Server. We used versi 2008AA f UMLS. As a result we btai ccepts that have the surce tlgy term as their syym. We assig a similarity value f 0.99 if the surce tlgy terms are syyms f the same ccept ad 0 therwise. The cmbiati algrithm used fr OAEI 2008 is a maximum-based algrithm. The similarity value fr a pair f ccepts is the maximum value btaied frm TermWN ad UMLSKSearch fr this pair f ccepts. As i the full SAMBO ad SAMBOdtf systems, SAMBO uses sigle threshld filterig ad SAMBOdtf duble threshld filterig. 4 Results fr the Aatmy Case The results fr the participatig systems ad discussis are available frm ad the paper [1]. Fr SAMBO ad SAMBOdtf tests were perfrmed a IBM R61i Laptp, WiXP Itel(R) Petium(R) Dual 1.73GHz, 1.73GHz, 1.99G RAM. Task 1. We used the matchers, cmbiatis ad filterig described i secti 3. SAMBO used threshld 0.6. SAMBOdtf used upper threshld 0.8 ad lwer threshld 0.4. These threshlds were chse based ur experiece with previus experimets with bimedical tlgies. SAMBO geerated 1465 mappig suggestis ad reached a precisi f 0.869, a recall f ad a f-value f Further, it reached a recall+ f This was the best result fr all 9 participatig systems i OAEI I 2007 we used a versi f SAMBO that used Term istead f TermWN ad a previus versi f UMLS. The 2007 versi btaied a better recall fr -trivial mappigs, but at the cst f a verall decrease i precisi ad recall. A pssible explaati fr this is ur strategy fr chsig maximum e mappig suggesti per ccept. I 2008 exact matchig strigs were preferred, while i 3 The system with best f-measure i 2007 (AOAS [23]) btaied precisi, recall, recall+ ad f-measure. SAMBO (i its first participati) was secd best system i 2007 regardig precisi, recall ad f-value, but best regardig recall+ [20] there was preferece betwee pairs that had exact matchig strigs r pairs that were prpsed based dmai kwledge. SAMBOdtf geerates 1527 mappig suggestis. Of these suggestis, 1440 have a similarity value betwee 0.6 ad 0.8. This meas that SAMBOdtf filtered ut 25 f the suggestis btaied by SAMBO with threshld 0.6. (A maual check seems t suggest that mst f these are crrectly filtered ut, but sme are wrgly filtered ut. Oe reas fr remvig crrect suggestis is that the surce tlgies have missig is-a liks.) Further, SAMBOdtf als filtered ut 19 suggestis with similarity values betwee 0.4 ad 0.6. (A maual check seems t suggest that these were crrectly filtered ut.) SAMBOdtf btaied a precisi f 0.831, a recall f 0.833, a f-value f ad a recall+ f This was the secd best result fr all 9 participatig systems i OAEI The ruig time fr SAMBO was ca 12 hurs ad fr SAMBOdtf ca 17 hurs. As discussed i [1] the best perfrmig systems i 2007 ad 2008 i terms f quality f the mappig suggestis heavily use dmai kwledge. This cmes at a cst f larger ruig time. Hwever, ather iterestig bservati discussed by the rgaizers f the aatmy track at OAEI 2008 was that circa 50% f the -trivial mappigs was fud by at least e system usig dmai kwledge ad at least e system that did t use dmai kwledge. Circa 13% f the -trivial mappigs was fud ly by systems usig dmai kwledge. Circa 13% f the -trivial mappigs was fud ly by systems that did t use dmai kwledge. The reas fr this is that the used dmai kwledge (mst fte UMLS) is t cmplete. Further, still circa 25% f the trivial mappigs were t fud at all. As [1] suggests, a cmbiati f differet strategies may imprve the results. Takig the ui f the SAMBO results with the results f the RiMOM [24] ad Lily [22] systems wuld give a higher recall ad recall+. RiMOM ad Lily use liguistic ad structure-based appraches, but dmai kwledge. Tasks 2 ad 3. We did t participate i tasks 2 ad 3. As reprted i [1] the best system fr task 2 (Ri- MOM) btaied a precisi f (with a recall f 0.677). The best system fr task 3 (RiMOM) btaied a recall f (with a precisi f ad a recall+ f 0.538). We te that the best recall is lwer tha the recall fr SAMBO ad SAMBOdtf i task 1. The best system fr task 3 fr -trivial mappigs (Lily) btaied a recall+ f (with a recall f ad a 17

6 precisi f 0.490). As either RiMOM r Lily used dmai kwledge, these ca be csidered t be gd results. Task 4. Fr task 4, we augmeted SAMBO ad SAMBOdtf i the fllwig ways. Fr SAMBO we added the mappigs i the partial referece aligmet t the list f mappig suggestis, but with a special status. These mappigs culd t be remved i ay filterig step. SAMBO geerated 1494 suggestis f which 988 are als i the partial referece aligmet. SAMBO btaied the best results f the participatig systems. With respect t the ukw part f the referece aligmet, its precisi icreased with 0.024, its recall decreased with ad its f- value icreased with Our strategy fr usig the partial referece aligmet helped remve wrg suggestis that cflicted with the partial referece aligmet, althugh als sme crrect suggestis were remved. Fr SAMBOdtf we als added the mappigs i the partial referece aligmet t the list f mappig suggestis with the special status. I additi, we used the partial referece aligmet i the duble threshld filterig step. We used a part f the partial referece aligmet that satisfied the csistet grup prperty as a csistet suggesti grup. Fr upper threshld 0.8 ad lwer threshld 0.4 we btaied 1547 mappig suggestis. SAMBOdtf btaied the secd best results f the participatig systems. With respect t the ukw part f the referece aligmet, its precisi icreased with 0.040, its recall with ad its f- value with SAMBOdtf was the system with the highest icrease i f-value ad was the ly system that used the partial referece aligmet t icrease bth precisi ad recall. This result is mst likely due t the fact that, i ctrast t task 1 where the csistet suggesti grup csists f suggestis, i this task the csistet suggesti grup csists f true mappigs. Therefre, the suggestis with similarity value betwee the tw threshlds that are retaied are structurally reasable with respect t true mappigs ad t just (althugh with high cfidece) suggestis. We te that althugh the imprvemets seem small, as SAMBO ad SAMBOdtf perfrm already well their w, eve small imprvemets are valuable. Further, due t the chice f the partial referece aligmet all ewly fud mappigs are -trivial. I a fllw-up task 4 we have started ivestigatig the use f partial referece aligmets i the differet cmpets f the framewrk i secti 2.1 [10]. I additi t the techiques described abve, we have used partial referece aligmets i a preprcessig step, t defie ew matchers ad i ew filterig steps. 4 I the preprcessig appraches we ivestigate whether we ca use a partial referece aligmet t partiti the tlgies it mappable parts ad test whether, i additi t the fact that we d t have t cmpute similarity values betwee all terms frm the first tlgy ad all terms frm the secd tlgy, this als leads t a better quality f the mappig suggestis. I the first apprach we partiti the tlgies it mappable parts usig the partitiig step f the duble threshld filterig described i secti 2.2 ad [2]. A part f the partial referece aligmet satisfyig the csistet grup prperty is used as a csistet grup. Further, accrdig t ur experiece i aligig tlgies we kw that the structure f the surce tlgies is t always perfect. Fr istace, give the tw tlgies ad the partial referece aligmet i the aatmy case f OAEI 2008, it ca be deduced that may is-a relatis are missig i at least e f the surce tlgies. Based this bservati we experimet with a secd apprach where we add t the surce tlgies the missig is-a relatiships that ca be deduced frm the surce tlgies ad the partial referece aligmet. After this fixig f the surce tlgies the partial referece aligmet will satisfy the csistet grup prperty. As the ituiti f the preprcessig step is t partiti the tlgies it mappable parts, we ca ly geerate mappig suggestis that are reasable frm a structural pit f view. This suggests that, whe usig a preprcessig step, the precisi may becme higher as suggestis that d t cfrm t the structure f the surce tlgies cat be made. As we add the partial referece aligmet t the result, the recall may be icreased as sme f the partial referece aligmet mappigs may t be fud by the base systems. Hwever, the similarity values betwee the terms d t chage ad it is therefre t likely that ew mappigs are fud. Fr threshlds 0.6 ad abve ur experimets crrbrate this ituiti. Ather bservati is that, ctrary t the ituiti, fixig the surce tlgies may lead t a decrease i recall. The reas fr this is the quality f the uderlyig tlgies where is-a is t always prperly used. Oe way t create a matcher based a partial referece aligmet, is t use uderlyig prperties f 4 Thaks t Christia Meilicke f the rgaizati cmmittee f OAEI Aatmy fr ruig ur ewly develped algrithms the aatmy data set. 18

7 the mappigs i the partial referece aligmet. We have previusly bserved that smetimes fr tw give surce tlgies, cmm patters ca be fud betwee the crrect mappigs. Fr istace, i the partial referece aligmet f the OAEI 2008 aatmy we fid the mappigs <lumbar vertebra 5, l5 vertebra> ad <thracic vertebra 11, t11 vertebra> which share a similar liguistic patter. Based this bservati we develped a matcher that augmets previusly geerated similarity values fr term pairs whe these term pairs display a similar (liguistic) patter as mappigs i the partial referece aligmet. Several ew crrect mappigs were fud. Fially, we als experimeted with a filter strategy that remves suggestis that d t have similar liguistic patters tha the mappigs i the partial referece aligmet. We expect therefre that sme crrect suggestis btaied thrugh UMLS will be remved ad therefre the recall may g dw. This is ideed the case i ur experimets. The precisi whe usig this filter apprach is, hwever, always higher r equal t the precisi fr SAMBO. This is because the suggestis that had a liguistically similar patter as mappigs i the partial referece aligmet were usually crrect. 5 Cclusi We have briefly described ur tlgy aligmet systems SAMBO ad SAMBOdtf ad their results fr the aatmy aligmet tasks f OAEI. We have used a cmbiati f UMLSKSearch ad TermWN ad btaied the best results i OAEI aatmy 2008 with respect t quality f the suggestis. Hwever, as the recall+ f the best system is still arud 0.6, wrk still eeds t be de t fid -trivial mappigs. Ather prblem that we ivestigate is whether systems that d well i the aatmy case will als perfrm well fr ther cases. Mre large-scale evaluati is eeded i the area. Further, the OAEI cases ly prvide a bechmark fr part I f the framewrk described i secti 2.1. Nt s much wrk has bee de user ivlvemet, user iterfaces ad tlgy ad tlgy aligmet visualizati [9, 5]. Als, give the fact that differet algrithms seem t d differetly well fr differet kids f tlgies ad evaluati measures, a majr prblem is decidig which algrithms shuld be used fr a give aligmet task. This is a prblem that users face, ad that we have als faced i the evaluati. Recmmedati strategies [19, 14, 3] may alleviate this prblem. Other challeges fr the tlgy aligmet field are give i [17]. Refereces [1] C Caraccil, J Euzeat, L Hllik, R Ichise, A Isaac, V Malaise, C Meilicke, J Pae, P Shvaik, H Stuckeschmidt, O Svab-Zamazal, ad V Svatek. Results f the tlgy aligmet evaluati iitiative I Prceedigs f the Third Iteratial Wrkshp Otlgy Matchig, [2] B Che, H Ta, ad P Lambrix. Structure-based filterig fr tlgy aligmet. I Prceedigs f the IEEE WETICE Wrkshp Sematic Techlgies i Cllabrative Applicatis, pages , [3] M Ehrig, S Staab, ad Y Sure. Btstrappig tlgy aligmet methds with APFEL. I Prceedigs f the Iteratial Sematic Web Cferece, pages , [4] J Euzeat ad P Shvaik. Otlgy Matchig. Spriger, [5] S Falcer ad M-A Strey. A cgitive supprt framewrk fr tlgy mappig. I Prceedigs f the 6th Iteratial Sematic Web Cferece, pages , [6] A Gómez-Pérez. Otlgical egieerig: A state f the art. Expert Update, 2(3):33 43, [7] R Jasper ad M Uschld. A framewrk fr uderstadig ad classifyig tlgy applicatis. I Prceedigs f the 12th Wrkshp Kwledge Acquisiti, Mdelig ad Maagemet, [8] Y Kalfglu ad M Schrlemmer. Otlgy mappig: the state f the art. The Kwledge Egieerig Review, 18(1):1 31, [9] P Lambrix ad A Edberg. Evaluati f tlgy mergig tls i biifrmatics. I Prceedigs f the Pacific Sympsium Bicmputig,pages ,

8 [10] P Lambrix ad Q Liu. Usig partial referece aligmets t alig tlgies. I Prceedigs f the 6th Eurpea Sematic Web Cferece, [11] P Lambrix ad H Ta. SAMBO - a system fr aligig ad mergig bimedical tlgies. Jural f Web Sematics, Special issue Sematic Web fr the Life Scieces, 4(3): , [12] P Lambrix, H Ta, ad Q Liu. SAMBO ad SAM- BOdtf results fr the tlgy aligmet evaluati iitiative I Prceedigs f the Third Iteratial Wrkshp Otlgy Matchig, pages , [13] P Lambrix, H Ta, ad W Xu. Literaturebased aligmet f tlgies. I Prceedigs f the Third Iteratial Wrkshp Otlgy Matchig, pages , [14] M Mchl, A Jetzsch, ad J Euzeat. Applyig a aalytic methd fr matchig apprach selecti. I Prceedigs f the Wrkshp Otlgy Matchig, [20] H Ta ad P Lambrix. SAMBO results fr the tlgy aligmet evaluati iitiative I Prceedigs f the Secd Iteratial Wrkshp Otlgy Matchig, pages , [21] T Wächter, H Ta, A Wbst, P Lambrix, ad M Schreder. A crpus-drive apprach fr desig, evluti ad aligmet f tlgies. I Prceedigs f the Witer Simulati Cferece, pages , Ivited ctributi. [22] P Wag ad B Xu. Lily: tlgy aligmet results fr OAEI I Prceedigs f the Third Iteratial Wrkshp Otlgy Matchig, pages , [23] S Zhag ad O Bdereider. Hybrid algiemet strategy fr aatmical tlgies: results f the 2007 tlgy aligmet ctest. I Prceedigs f the Secd Iteratial Wrkshp Otlgy Matchig, pages , [24] X Zhag, Q Zhg, J Li, ad J Tag. RiMOM results fr OAEI I Prceedigs f the Third Iteratial Wrkshp Otlgy Matchig, pages , [15] NF Ny. Sematic itegrati: A survey f tlgy-based appraches. Sigmd Recrd, 33(4):65 70, [16] P Shvaik ad J Euzeat. A survey f schemabased matchig appraches. Jural Data Sematics, IV: , [17] P Shvaik ad J Euzeat. Te challeges fr tlgy matchig. I Prceedigs f the 7th Iteratial Cferece Otlgies, Databases, ad Applicatis f Sematics, [18] H Ta, V Jakieė, P Lambrix, J Aberg, ad N Shahmehri. Aligmet f bimedical tlgies usig life sciece literature. I Prceedigs f the Iteratial Wrkshp Kwledge Discvery i Life Sciece Literature, LNBI 3886, pages 1 17, [19] H Ta ad P Lambrix. A methd fr recmmedig tlgy aligmet strategies. I Prceedigs f the 6th Iteratial Sematic Web Cferece, LNCS 4825, pages ,