This paper introduces the ontology mapping approach of a system that automatically integrates data sources into an ontology-based data integration system (OBDI). In addition to the target and source ontologies, the mapping algorithm requires a SPARQL query to determine the ontology mapping. Further, the mapping algorithm is dynamic: running each time a query is processed and producing only a partial mapping sufficient to reformulate the query. This approach enables the mapping algorithm to exploit query semantics to correctly choose among ontology mappings that are indistinguishable when only the ontologies are considered. Also, the mapping associates paths with paths, instead of entities with entities. This approach simplifies query reformulation. The system achieves favorable results when compared to the algorithms developed for Clio, the best automated relational data integration system.
We have developed an Ontology-based Data Integration (OBDI) system that departs from the conventional OBDI organization. The goal is to include automatic integration of new data sources, provided those data sources publish a self-describing ontology. A consequence of that goal is there is no longer the opportunity for an engineer to review and correct an ontology matching prior to its use by the query reformulation system. As ontology matching is understood to be an uncertain process, some other method of mapping refinement is needed. Our system uses queries for this purpose.
Ontology mapping in conventional OBDI systems is determined prior to, and
without knowledge of the queries to be executed [
The organization was motivated by the following observations. A mapping method may determine that an entity in one ontology maps with equal likelihood to two or more entities in the other ontology. The mapping and reformulation of certain queries is correct only if one pairing is chosen. The correct choice may be different for different queries. The query itself may lend additional semantics that correctly resolve the ambiguity.
These observations are supported by the example in Fig. 2. Looking at the ontologies alone, there is insufficient information to determine if the class T :P eople should Query q
Query q Ontology T Ontology S
Ontology Mapping
Query Reformulation
Reformulated query
Ontology T Ontology S
Ontology Mapping
Query Reformulation
Reformulated query (a) Traditional (b) The proposed be mapped to S:T eacher or to S:SAsts-upadtheconrrtes.poAndence records the mapping confidenceabetween two ss-paanthys. mapthird possibility is one-to-m ping entailing both. However, givenGD0e,tfiahnsiest-ipoanthP9co(rSrSes-pPQoAnTdLeHncqCebOuetRewRereEynStwPionOsNsF-DpaiEtghNs.Cp2Ean().dcGp)i0v,(ednwetnwohoteigdrcabpyhπspaG,ps0a)knidss for the
S AR h time of the course that is offered byaPAt“uTpHle-iS<nETps,Gtp0e0,,caipnn>d”,cp,suiicshtathcoantficpdl∈eenGcaeRrmAePtaHhsu-rSeS.-PATH-SEeToGp,pl0e∈ GsRhAoPuH-lSdS- only be
E is at T :P mapped to S:teacher. A complemenWteasrayy pq∈uπep,rp0y,aanddpd0∈reπsp,sp0i.nWgeaslsotuusde eαnπp,tp0 etondreonoltel mtheecnontfi-requires dence measure, which is απp,p0 = cp. In the abaoveredeffionitriomn, uwelaastseumde qthueery can T :P eople to be mapped to S:studeconrretsp.onCdeoncrermeecasturaesneqsuwivaleenrcse. from be achieved only through mutuallyDeexfincitliuons1i0ve(MqAuTCeHryCAdNeDpIeDnATdEe).nGtivmen aapquperyingrgapsh.Tq, a graph
An overview of the matching alΩGgTioqs,Gcrai=ltle{hdπamp,pm0a:itpcsh∈acGasnRdAfidPoaHtle-lSionS-wtPeArmTssH.o-TSfEaoTsTeeqt,oxpf0pc∈olrGroeRsipAtoPntdHhe-nSecSe-sPcΩAoTTqnH,G-t,SewExhTeGtre}i,mplicit in a query, the algorithm maps paitfhthesfollowing conditions are saotinsfitedo: logy to paths in a source onin the target tology. A path contains a datatype a––nSGIdNisKmaGs⊆ubgraph of S;
uSlItNiKpSl;e classes connected by properties. Each element can be considered as the co–nfctooerrraelslpsosn-dpeanthcepπ∈p,pG0 R∈AΩPTHq,-GS,Sw-PheAerTeHp-0S∈EiTnTq,tthhere exists exact one ses-pxatahmple, if xt of other elem nts GRAPHe-SSp-PaAtThH.-SFEToGr; a path contains a class P eople, and–afcoorrraelslpsosn-dpeeantrhctepy0π∈p,tpG0e∈RaAΩPcTqH,G-S,Sw-hPeArTetHph-∈SeEGnTRGAw,PthHe-rSeSe-xPiAstTsHex-aScEtToTnqe; ss-paatth P eople prop hes, e can infer th is a T eacher instead of a Student–. f=oIrtSaOlflUopRalCilrEopo2wf,stshs-epatttwhhosapco1t,rprne2spo∈ontGdeRadAlsPls-Hpe-aStnhSs-tPipAt01T,ipeH02-sS∈EiGTnTRqA,oPifHnS-SeOSU-PpRACaTEtHph1- have a corresponding entity in the corresponSEdTiGn,πgp1,pp01a∈tΩhTq.,GP,πap2t,ph02∈-bΩaTsq,eG,dalsmoshaarpetpheisnamge smouruces,StOnUReCcEep01ssarily accommodate this. Since the number=oSOfUuRCnEcp02o;nstrained paths in a graph is much larger than the number of vertices, the properties suggest that path mapping is a combinatorially much harder problem. However, as the organization stipulates that ontology mapping is dynamic and specific to a query. Thus, the mapping may be limited to only those mappings required to reformulate the query. Relative to ontologies, queries are very small, and only the paths in the target ontology corresponding to the query need to be mapped. These constraints limit the search problem to a manageable size.
Consider the specific SPARQL query in Fig. 2(c). Fig. 2(d) illustrates the part of ontology that corresponds to q, which is called a query graph. The task is to generate mappings that can be used to reformulate q in terms of ontology S. The algorithm must address the following challenges. The class P eople in T can be mapped to T eacher or Student in S, and some entities, such as class Schedule in S, do not have any mapped entity in T . However, the mapping for Schedule is necessary to reformulate the query.
All of these challenges are met by mapping paths, represented as sequences of labels, where the sequence comprises alternating vertex and edge labels. In the example, query q has two paths in its query graph: {T :Course, T :teacher, T :P eople, T :name, string} and {T :Course, T :time, date}
We search for a subgraph with two paths in ontology S, which have the highest probability such that each of the paths in S are mapped to paths that correspond to the query graph of q. The probability of each path mapping is determined by scoring the similarity of all labels in the paths.
Mapping results should be: {T :Course,T :teacher, T :P eople, T :name, string}
= {S:Course, S:teachBy, S:T eacher, S:name, string} {T :Course,T :time, date}
= {S:Course, S:hasSchedule, S:Schedule, S:date, date}
Note that this mapping is specific to the query. If another query asks for the time
of the course that is taken by “Einstein”, the mapped path should contain S:Student
instead of S:T eacher. Thus, defining similarity to a sequence of labels identified by
the query introduces context. Limiting the problem to the paths in the query graph not
only limits the size of the computation, but also removes any consideration of
potentially conflicting interpretation. Given that sequence matching is an endemic problem
in genomic data processing, there are many avenues open to exploration.
Experimental Setup: The evaluation comprises real world ontologies from
bibliography domain. DBLP ontology is generated by direct mapping the relational schema of
the DBLP metadata database using Ultrawrap [
Baseline: Clio is a semi-automatic relational schema mapping system, however, the
resulting algorithms are applicable to ontologies [
(c) Bibliography, ClassAll the highest similarity between the sources of the paths. We also enhance the baselines, by keeping 2 mappings instead of 1 for each datatype property. We generate mapping for each of them, and consider the mapping as correct if any of them is correct. Results: Overall, our approach dominates over all baselines as shown in Fig. 3. For PathOnly queries, query precision and path precision are the same, and all approaches have 100% valid rate, because each query only consists of one path in the query graph. In terms of query precision and path precision, our approach is 0.15 higher than the best top1 baselines. The three top2 baselines are improved with respect to the top1 approaches. However, even though the top2 approaches choose the correct mapping from large number of candidate mappings, it would still not yield a mapping with higher precision than our approach. clio similar has better performance comparing to clio minimal and clio maximal. This indicates that the similarity between sources is important to the path mapping. For ClassAll queries, none of the baselines are competitive with respect to our approach. This is because the baselines determine the datatype property and source mappings first, and only use query to find valid path mappings. In some cases, the valid mappings are not existed. For our approach, the path mappings are jointly determined by both entity mappings and the query, so we have higher chances to find valid correct mappings. proposed approach clio_minimal_top1 clio_maximal_top1 clio_similar_top1 clio_minimal_top2 clio_maximal_top2 clio_similar_top2