Ben Yahia, S.

Mephu Nguifo, Engelbert

Slimani, Y.

Zghal, Sami

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SODA: an OWL-DL based ontology matching system

HAL-Artois

SODA: an OWL-DL based ontology matching systemSami Zghal1,2, Sadok Ben Yahia2, Engelbert Mephu Nguifo1, and Yahya Slimani21 CRIL CNRS FRE 2499, Artois University, IUT of LensRue de l’Universite´ - S.P. 16, 62307 Lens Cedex, France{zghal, mephu}@cril.univ-artois.fr2 Computer Science Department, Faculty of Sciences of Tunis, TunisiaCampus Universitaire, 1060 Tunis, Tunisia{sadok.benyahia, yahya.slimani}@fst.rnu.tnAbstract. This paper describes SODA a novel ontology alignment method for theOWL-DL format. SODA uses a new approach that consists in computing local andsemantic similarities among ontological elements.1 Presentation of the systemSODA [1] (Structural Ontology OWL-DL [2] Alignment), is a new approach that alignstwo OWL-DL ontologies using similarity measures [3]. Both OWL-DL ontologies aretransformed in two corresponding graphs DL-GRAPH which describe all informationin the ontologies. SODA uses the DL-GRAPH to align the two ontologies. It operatesinto successive steps. The first step, computes local similarity by means of linguisticand structural similarities, whereas the second one computes the semantic similarity.Figure 1 depicts the architecture of SODA system.1.1 Specific techniques usedEach OWL-DL ontology to be aligned is transformed into a non oriented graph calledDL-GRAPH. All the information belonging to OWL-DL ontology are faithfully mappedinto the DL-GRAPH. Nodes of the proposed graph represent classes, properties and in-stances. The DL-GRAPH nodes represent six types (named also categories) of entitiesthat may exist in an OWL-DL ontology: i.e., concepts, instances of concepts, data types,values of data types and class properties (object nature and data type nature). Connec-tions between the graph nodes map the relationships between the entities in an OWL-DLontology. It is worthily noted that an OWL-GRAPH describes all the semantic relationsbetween different entities of an ontology. A graph DL-GRAPH allows to represent fourkinds of links: specialization, attribution, instantiation and equivalence. DL-GRAPHSare exploited by the alignment model SODA. Similarity measures are used to comparethe components of the graphs in order to obtain the correspondence between them.Nodes and links of the two graphs are compared to get out the correspondence betweendifferent ontological entities using similarity measures. The output algorithm is an RDFfile containing all the correspondences between the entities and the similarity measurevalues.		 		  		Fig. 1. Architecture of SODASODA explores the structure of DL-GRAPH to compute the similarity values be-tween the nodes of both ontologies. The alignment model associates to each categoryof nodes an aggregation function. This function takes in consideration all the similaritymeasures and the structure of couple of nodes to be matched. This aggregation functionexplores all descriptive information of nodes.SODA operates into two successive steps: local and semantic. The first step, im-plemented via PHASE1 LINGSIM (see Algorithm 1) and PHASE2 STRUCTSIM (seeAlgorithm 2) functions, computes the local similarity (linguistic and structural one).The second step, c.f. the PHASE3 SEMSIM function (see Algorithm 3), computes thesemantic similarity. Table 1 summarizes the notations that are used in the description ofour algorithms.Local similarityThe computation of the local similarity is carried out in two phases. The first phaseallows to compute the linguistic similarity for each couple of node of the same category.The second phase allows to compute the structural similarity using the structure ofneighbors of the nodes to be aligned.- O1,O2: two OWL-DL ontologies for alignment- VLS : linguistic similarity vector- VSS : structural similarity vector- VV SEMS : semantic similarity vectorEach node of the ontology is characterized by:- Type: node type- Name: node nameEach element of the vectors VSL, VSS et VV SEM is characterized by:- Node 1: the node of ontology O1- Node 2: the node of ontology O2- Sim: the similarity valueTable 1. Algorithm notationsAlgorithm 1 (c.f., PHASE1 LINGSIM function) computes the linguistic similaritymeasure. The name of properties and instances are used to compute linguistic simi-larity. For classes, the computation of linguistic similarity integrates also commentsand labels. The computation of linguistic similarity is done only once for each nodeof the same category. JARO-WINKLER or MONGE-ELKAN [4] functions are used tocompute the linguistic similarity. JARO-WINKLER measure is more adapted for shortstrings, like those representing names and labels [4]. Besides, MONGE-ELKAN measureis better indicated for long strings, e.g. the comments [4]. PHASE1 LINGSIM functioncomputes the linguistic similarity of couple of nodes of both considered ontologies.PHASE1 LINGSIM function takes as input the two ontologies O1 and O2 and the lin-guistic similarity function FunctLS . COMPUTELINGSIM function (c.f., line 8 of Algo-rithm 1) takes as an input two nodes, Node1 et Node2, and linguistic similarity func-tion. PHASE1 LINGSIM function returns as an output linguistic similarity value SimL.This function implements the JARO-WINKLER or the MONGE-ELKAN measures. Lin-guistic similarity of the different couples of nodes are used after that in the computationof the structural similarity.Structural similarity is computed by using linguistic similarity of the couple ofnodes to align and the neighborhood structure. Adjacent neighbor nodes of the entitiesare grouped by category, c.f. PHASE2 STRUCTSIM. This function takes as input twoontologies O1 and O2 to align, linguistic similarity vector VLS and weights associatedfor each category ΠC . EXTRACTNODES function, (c.f., lines 9 - 11 of Algorithm 2),allows to extract for each node, its neighbors and to put them in VNodei , where Nodeiis a node of O1 or O2. VNode1 and VNode2 vectors and weights associated for each cate-gory, ΠC , are used by the COMPUTESTRUCTSIM function (c.f., line 13 of Algorithm 2)to compute the structural similarity, SimS . To work out, the following ”Match-Basedsimilarity” [5, 6] is used to compute similarity between two categories (one in the firstontology and the other in second one):MSim(E,E′) =∑(i,i′)∈Paires(E,E′) Sim(i, i′)Max(|E|, |E′|) ,Function : PHASE1 LINGSIM1Data:1. O1 and O2 : two ontologies to align2. FunctLS : linguistic similarity functionResults: VLS : linguistic similarity vectorBegin2/*Parse all nodes of the ontology O1*/3forall (Node1 ∈ O1) do4/*Parse all nodes of the ontology O2*/5forall (Node2 ∈ O2) do6If Node1.type=Node2.type then7SimL = COMPUTELINGSIM(Node1,Node2)8/*Add Node1,Node2 and SimL to VLS*/9Add((Node1,Node2,SimL),VLS)10return(VSL)11End12Algorithm 1: PHASE1 LINGSIMwhere E et E′ represent two sets of nodes belonging to the same category in O1and O2. This function uses the local similarities of the couple (i, i′) already computed.Structural similarity is computed by aggregating the ”Match-Based similarity” of eachgroup of adjacent neighborhood nodes by category. A weight is attributed for eachgroup to have a normalized structural similarity. Each category has the same weightwhich is equal to 1 over the number of groups (categories). Structural similarity, SimS ,is computed as follows:SimS =∑(E,E′)∈(VNode1 ,VNode2 )Π(E,E′)MSim(E,E′).The structural similarity, SimS , is normalized since∑(Π(E,E′)) = 1. Values of thelinguistic similarity vector, i.e. VLS , and structural similarity vector, i.e. VSS , alreadyobtained are combined to compute the semantic similarity.Semantic similarityThe semantic similarity is a combined similarity measure of linguistic and struc-tural similarities (local similarity). Algorithm 3 takes as input the two ontologies tobe aligned, the two similarity vectors (the linguistic one, VLS , and the structural one,VSS). It takes also the weights attributed to the linguistic similarity and structural sim-ilarity (ΠL and ΠS =). Optimal weight values can be determined through severalexperiments. Algorithm 3 outputs semantic similarity vector, VSEMS . EXTRACTSIMfunction (c.f., lines 9 - 11 of Algorithm 3) extracts corresponding values of similaritiesof the nodes (Node1 or Node2) from similarity vectors (VLS or VSS). Each couple ofentities, Node1 and Node2, of the same category, the semantic similarity is computedas follows (c.f., line 13 of Algorithm 3):SimSEM (e1, e2) = ΠLSimL(e1, e2) +ΠSSimS(e1, e2).1.2 Link to the system and parameters fileAt the following URL the system SODA can be downloaded: www.cril.univ-artois.fr/˜mephu/OAEI2007/systemSODA.rar.Function : PHASE2 STRUCTSIM1Data:1. O1 et O2: two ontologies to align2. VLS : linguistic similarity vector3. ΠC : weights for each category of nodeResults: VSS : structural similarity vectorBegin2/*Parse all nodes of the ontology O1*/3forall (Node1 ∈ O1) do4/*Parse the nodes of the ontology O2*/5forall (Node2 ∈ O2) do6If Node1.type==Node2.type then7/*Extract form VNode1 neighbor nodes Node1*/8VNode1=EXTRACTNODE(Node1)9/*Extract form VNode2 neighbor nodes Node2*/10VNode2=EXTRACTNODE(Node2)11/*Compute the structural similarity*/12SimS = COMPUTESTRUCTSIM(VNode1 ,VNode2 ,ΠC )13/*Add Node1, Node2 and SimS to VSS*/14ADD((Node1,Node2,SimS),VSS)15return(VSS)16End17Algorithm 2: PHASE2 STRUCTSIM1.3 Link to the set of provided alignmentsAt the following URL the contest results of SODA are available: www.cril.univ-artois.fr/˜mephu/OAEI2007/resultsSODA.rar. As the evaluation process of OAEI 2007 Chal-lenge was over, it is not possible to comment those results here. Some details of resultscan be found in [1], especially for the benchmark dataset.2 ConclusionIn this paper, a new ontology alignment system for OWL-DL format is described. Re-sults obtained on OAEI 2007 benchmarks and directory are available on website. Eval-uation of benchmarks dataset are also in [1]. SODA, finds ontological entities to bealigned using local and semantic similarities. In further research, we plan to tacklealignment of large ontologies.Function : PHASE3 SEMSIM1Data:1. O1 et O2: two ontologies to align2. VLS : linguistic similarity vector3. VSS : structural similarity vector4. ΠL and ΠS : the respective weights of linguistic and structural similarityResults: VSEMS : semantic similarity vectorBegin2/*Parse all nodes of the ontology O1 */3forall (Nœud1 ∈ O1) do4/*Parse all nodes of the ontology O2*/5forall (Nœud2 ∈ O2) do6If Node1.type==Node2.type then7/*Extract the linguistic similarity of Node1 and Node2 from VLS*/8SimL=EXTRACTSIM(VLS ,Node1,Node2)9/*Extract the linguistic similarity of Node1 and Node2 from VSS*/10SimS=EXTRACTSIM(VSS ,Node1,Node2)11/*Compute the semantic similarity*/12SimSEM = ΠLSimL +ΠSSimS13/*Add Node1, Node2 and SimSEM to VSEMS*/14ADD((Node1,Node2,SimSEM ),VSEMS)15return(VSG)16End17Algorithm 3: PHASE3 SEMSIMReferences1. Zghal, S., Ben Yahia, S., Mephu Nguifo, E., Slimani, Y.: SODA : Une approche structurellepour l’alignement d’ontologies OWL-DL. In: Proceedings of the first French Conference onOntology (JFO 2007), Sousse, Tunisia (October 2007)2. Smith, M.K., Welty, C., Mcguinness, D.L.: OWL: Ontology Web Language Guide. Techni-cal report, W3C: Word Wide Web Consortium, http://www.w3.org/TR/2004/REC-owl-guide-20040210/ (February 2004)3. Euzenat, J., Mochol, M., Shvaiko, P., Stuckenschmidt, H., Svab, O., Svatek, V., van Hage,W.R., Yatskevich, M.: Results of the ontology alignment evaluation initiative 2006. In: Pro-ceedings of the First ESWC 2006 international workshop on ontology matching. (2006)4. Monge, A., Elkan, C.: The field-matching problem: algorithm and applications. In: Proceed-ings of the 2nd International Conference on Knowledge Discovery and Data Mining. (1996)267–2705. Touzani, M.: Alignement des ontologies OWL-Lite. Master’s thesis, University of Montreal(2005)6. Euzenat, J., Loup, D., Touzani, M., Valtchev, P.: Ontology alignement with OLA. In: Pro-ceedings of the 3rd International Workshop : Semantic Web Conference EON, Hirochima,Japan (November 2004) 341–371AcknowledgementsThis work is partially supported by the French-Tunisian project PAI CMCU 05G1412.Thanks to Amine FERJANI and Marouen KACHROUDI for implementing the method.

SODA: an OWL-DL based ontology matching system

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