This paper describes ONTMAT an ontology matching system, and presents the results obtained for the Ontology Alignment Evaluation Initiative (OAEI) 2017. ONTMAT is an ontology matching process, which compares the instances of ontologies to align in order to deduce the relations between their concepts. Then, based on hierarchical and binary relations between the concepts inside the ontologies it performs entities matching.
1.1
ONTMAT uses a terminological methods based on WordNet dictionary [
Furthermore, the TAM elements and the concepts neighbors of AM are compared
by using the inference roles with the terminological techniques cited previously and
the retained alignment will be added to AM. The concepts neighbors are those related
by hierarchical or binary relations with AM concepts. Here, we first align the
neighboring concepts because they have more chance to be similar [
In our proposition we suppose that Wordnet is hierarchically organized as
W(S,≤,Ag,g), where S is a set of synsets {s1,s2,…,si}(i is a positive integer), and an
annotate function Ag associates the gloss g to each synset. Furthermore, the relations
≤ between concepts , may be presented in the following logical relations [
The entities aligned can be related by one of the hierarchical relation presented in
the set , , where ( : equivalence; subclass), fuzzy relation
symbolized by “&”, or binary relation. Further, the binary ontologies relations (O1, O2)are
also aligned by an element of the set HR. The algorithm will explain in the following
items:
1. In level 1 we compare the instances names (IO1,IO2) of ontologies( , )to deduce
the relations among their concepts. To do this, WordNet is exploited because we
cannot assume with certainty that two entities are dissimilar if they have different
names (synonyms), or they are equivalent if they have the same name(homonyms).
If the name does not existed in WordNet we will measure the similarity among
names by the n-gram measure. Then the equivalent instances will construct the
instances matrix IM. The concepts (C1,C2) of ( , ) that have the same sets of
instances in IM are considered as equivalent concepts as proven in [
Next the results obtained will be combined with inferences methods illustrated in
[
ment Binary Relation Source Matrix) ─ If ( , , ( , , then ( , ,
Binary Relation Target Matrix)
will be inserted to ABRSM
(Alignwill be added to ABRTM (Alignment
Thus, binary relations can be aligned because we have: hR Iff dom( ) hR
dom( ) and ran( ) hR ran( ); where [
We have adapted the format of the alignment result to the reference alignments restricted to name classes, using the “=” sign for equivalence relation with confidence of 1. Although our system provides other relations as subsumption, and binary relations without measure, as well as the alignment of binary relation by the HR. 2
In this version we wish to test the techniques used by ONTMAT, such as, the inferences mechanisms applied onWordNet and the ontologies source and target, and the deduction of the matching among entities based on instances. The most appropriate track to do these tests is the conference track.
Conference track comprises 16 ontologies from the domain of conference organization. Most ontologies of this track were equipped with OWL DL axioms; which is useful to test our inferences approach. Table 1shows the evaluation result obtained by running ONTMAT under the SEALS client with the command: java -jar F:/temp/seals-omt-client.jar F:/temp/ONTMAT -t
This command tests two predefined ontologies from the Conference. From Table 1 we can write that ONTMAT perform well because these ontologies are the same structure.
The results obtained by the global test as illustrated in Table 2, are not well as the results of the precedent table in term of precision and F-measure. Although, the global recall is 0.434. cmt-ekaw cmt-iasted cmt-sigkdd conference-confof conference-edas conference-ekaw conference-iasted conference-sigkdd confof-edas confof-ekaw confof-iasted confof-sigkdd edas-ekaw edas-sigkdd ekaw-iasted ekaw-sigkdd iasted-sigkdd Global
The application seals-omt-client from seal, only test files where the alignment relation between concepts is itself the equivalence relation. However ONTOMAT, offers other possibilities in terms alignment relations between entities such as; & : Fuzzy and binary relations. We hope that OAEI takes into consideration those types of relations in the reference alignment file. 3
We have briefly described the mechanisms exploited by our proposition ONTMAT, and presented the results obtained under the conference track of OAEI 2017. This is our first participation in OAEI, the results are not satisfying, and the system presents some limitations. In the future, we will make great efforts to improve ONTMAT results, and participate in more tracks.