and other research outputs Expliciting semantic relations between ontologies in large ontology repositorie

Allocca, Carlo

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Open Research OnlineThe Open University’s repository of research publicationsand other research outputsExpliciting semantic relations between ontologies inlarge ontology repositoriesConference or Workshop ItemHow to cite:Allocca, Carlo (2009). Expliciting semantic relations between ontologies in large ontology repositories. In:The 6th Annual European Semantic Web Conference (ESWC2009), 31 May - 04 Jun 2009, Heraklion, Crete, Greece.For guidance on citations see FAQs.c© 2009 The AuthorVersion: Version of RecordCopyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukExpliciting Semantic Relations BetweenOntologies in Large Ontology RepositoriesCarlo Allocca?Knowledge Media Institute (KMi), The Open University, Walton Hall,Milton Keynes MK7 6AA, United Kingdomc.allocca@open.ac.ukAbstract. Our goal is to develop a framework for detecting and man-aging semantic relations between ontologies for large ontology repos-itories. Making explicit implicit relations between ontologies providesmeta-information that facilitates the development of Semantic Web Ap-plications. The development of the framework relies on an ontology-basedmethodology. This approach offers several advantages, among which thepossibility to reason upon ontologies and their relations.1 MotivationSemantic Web Applications (SWAs) use the Semantic Web (SW) as a large-scaleknowledge source [1]; they achieve their tasks by automatically retrieving andexploiting knowledge from the SW as a whole using advanced Semantic WebSearch Engines (SWSEs) such as WATSON [1]. On the other hand, becausethey are built and applied in different contexts, ontologies are not standaloneinformation artifacts: they are linked to one another through semantic relations.This aspect has been totally ignored by current SWSEs, including WATSON.For example, the query “student” currently gives 1079 ontologies as a result inWATSON (valid on the 25/03/2009). However, already in the first page, at least 2of the ontologies (http://www.vistology.com/ont/tests/student1.owl andhttp://www.vistology.com/ont/tests/student2.owl) represent, apart fromtheir URIs and the base namespaces, exactly the same logical model, expressedin the same ontology language. Another common situation is when an ontologyhas been translated in different ontology languages, like it is the case of thefirst and second results of the query “student, university, researcher”(http://reliant.teknowledge.com/DAML/Mid-level-ontology.owl and http://reliant.teknowledge.com/DAML/Mid-level-ontology.daml). In that case, itis obvious that these two ontologies are in fact two different encodings of thesame model. Inspecting the results of WATSON in the same way, it is not hardto find ontologies connected by other, more sophisticated semantic relations:versioning, inclusion, similarity, etc. Leaving implicit these relations betweenontologies in SWSE’s ontology repositories generates additional difficulties inexploiting their results, leaving to the users and the applications to find what isthe right ontology, such as the latest version, to achieve their goal.? This work was funded by the EC IST-FF6-027595 NeOn Project2 Related WorkJ. Heflin [5], was the first to study formally some of different types of link be-tween ontologies, focusing on the crucial problems of versioning and evolution.Currently, there is no Ontology Management Systems that implements his frame-work. J. Hartmann [4] has proposed OMV (Ontoloy Metadata Vocabulary) asa standard that provide some external information/properties to the ontologiesfor supporting the reuse of ontologies. Among the expressed OMV propertiesthere are some relations that might be useful to simplify the procedure of au-tomatic discovering. The authors of [8] characterized, at a very abstract level, anumber of relations between ontologies such as sameConceptualization, Resem-blance, Simplification and Composition, without providing concrete elements fordetecting them. Several approaches have been focusing on how to compare twodifferent versions of ontologies in order to find out the differences. In partic-ular, PROMTDIFF [9] compares the structure of ontologies and OWLDiff(http://semanticweb.org/wiki/OWLDiff) computes the differences by entail-ment checking of the two set of axioms. SemVersion [10] compares two on-tologies and compute the differences at both the structural and the semanticlevels. In addition, there exist many measures to compute the similarity of twoontologies [2]. While existing work provides partial answers to the problem of de-tecting (some of the) relations between ontologies, what is needed is a complete,homogeneous framework for detecting, managing, reasoning with and exploitingthe links that implicitly relate ontologies on the Web.3 Proposed ApproachOur framework is based on an ontology-based methodology. It means to builda semantic structure, called the Ontology Semantic Relation (OSR) Ontology,providing an explicit representation of the implicit relations between ontologies.There are several advantages in using this approach: 1) It is flexible enough toadd a new relation at any time, without strong changes in the overall system;2) We can apply reasoning to infer new relations from elementary detectionmechanisms; 3) common ontology based infrastructures can be used to store,manipulate and query relations between ontologies.We have designed an architecture for our framework, as depicted in Figure 1.The OSR-Ontology separates the on-line part of the architecture–providing APIsand Services that rely on a reasoner–from the off-line part–populating the OSR-Ontology. The population of the ontology is based on three component: theControl Component (CC), the Detecting Component (DC) and the PopulatingComponent (PC). First, the CC selects from the Ontology Repository ontologiesthat need to be evaluated to establish potential relations. Second, the selectedset of ontologies is processed by the DC, which contains the main mechanisms todiscover the possible relations between the ontologies. Finally, the PC populatesthe semantic structure with the detected relations.Fig. 1. Architecture of the framework.4 ResultThe first results concerning this work are: 1) The OSR-Ontology; 2) The Detec-tion Component; and 3) A first evaluation.4.1 OSR-OntologyA first analysis of the implicit relations between ontologies has led us to distin-guish two sets of relations, here called atomic and complex. Both are formalizedin the OSR-Ontology structure downloadable at htt://kannel.open.ac.uk/ontology. The first set contains relations, which do not depend on other rela-tions. The second set, instead, contains relations which are defined using atomicrelations or other complex relations. In particular we discuss the following rela-tions:Semantic Relation: Let OR the collection of ontologies on the Semantic Web. AnSemantic Relation is any binary relation defined over OR.Inclusion and Equivalence: Inclusion, also known as Extention in the literature[5], is an example of atomic relation. It links together two ontologies if one is containedin the other. We distinguish two kinds of inclusions: syntactic (considering the set ofaxioms asserted in the ontologies) and semantic (also considering the entailments of theontologies). The Equivalence complex relation can be defined through a rule of the formEquivalence(O1, O2) : − Inclusion(O1, O2), Inclusion(O2, O1). Detecting equivalence(syntactic and semantic) is particularly important when ranking ontologies: the sameontology should not been ranked at several different places in the result set [3].Similar: Informally, similarity is a measure that expresses how close two ontologiesare. [2] describes various ways to compute the similarity between two ontologies, rele-vant in various application contexts. We included in the OSR-Ontology several notionsof similarity, starting from the three: SyntacticSimilarity, SemanticSimilarity and Vo-cabularySimilarity, working at the axiom, entailment and lexical levels respectively.PrevVersion: Linking an ontology to its previous version is of particular importance[7]. In accordance with the definition in [7] we distinguish: 1) PrevVersionConcep-tualChange: a change in the way a domain is interpreted; 2) PrevVersionExplica-tionChange: a change in the way the conceptualization is specified, without changingthe conceptualization itself.4.2 Detecting ComponentBelow, we report on some semantic relations for which we have implemented detectionmechanisms, and that are evaluated in the next section12SyntacticInclusion compares the ontologies at the structural level, checking if theset of axioms in O1 is included in the set of axioms in O2.SemanticInclusion compares the ontologies at the semantic level, checking if the setof axioms in O1 is entailed by the set of axioms in O2.SyntacticSimilar: The method is based on the metric (1), where T is a given param-eter.|setAxioms(O1)T setAxioms(O2)|max(|setAxioms(O1)|, |setAxioms(O2)|) ≥ T (1)VocabularySimilar:The method is based on the metric (2).|V ocabulary(O1)TV ocabulary(O2)|max(|V ocabulary(O1)|, |V ocabulary(O2)|) ≥ T (2)SemanticSimilar: We note LC(O1, O2) the set of axioms of O1 that are logical con-sequences of O2. The method is based on the metric (3).|LC(O1, O2)TLC(O2, O1)|max(|setAxioms(O1)|, |setAxioms(O2)|) ≥ T (3)4.3 Initial EvaluationTo provide an initial evaluation of the current implementation of the detection mech-anisms, we collected 20 pairs of ontologies from WATSON’s repository and we appliedthe above detection methods on them. In Table 1 the results for each evaluated methodare showed.Method Nb. Relations Average Time (ms) Manual eval.SyntacticInclusion 8 2 8SemanticInclusion 9 80 9SyntacticEquivalent 4 5 4SemanticEquivalent 6 180 6SyntacticSimilar 15 3 15VocabularySimilar 17 1 17Table 1. Evaluation results for the detection mechanisms.The second column contains the number of pairs of ontologies related by the corre-sponding relation according to the detection mechanisms (similarity is computed witha threshold T=0.5). The last column represents the result of a manual evaluation ofthe relations between the pairs of ontologies. Although, we have considered only a fewpairs of ontologies, containing on average 1000 axioms, the analysis of the result in1Note that when comparing two ontologies, to avoid computing all consequences (which is notalways possible [6]) our methods only check if the set of axioms of the first ontology is entailedby the second ontology and vice-versa.2Note that these comparisons ignore the URIs and base namespace of the ontologies, as they donot have any influence when establishing the considered ontology relations.Table 1 shows that there is no discrepancy between the results produced from the au-tomatic method and the manual evaluation. Moreover, this result validates some of ourhypothesis. In particular, having two methods for detecting syntactic and semantic re-lations appears relevant as not all the semantic relations have been discovered throughthe syntactic techniques. For example, it means that two pairs of ontologies wheresemantically equivalent, while syntactically different. Furthermore, we can observe animportant difference, in terms of time, between syntactic and semantic mechanisms.The formers are very fast and can be easily applied over large ontology repositories.The latter are a lot more complex, and will require more efficient techniques to scaleto repositories like the one of WATSON.5 Conclusion and Future WorkAn first analysis of implicit relations between ontologies is presented. Given their im-portance for supporting the development of the SWAs, our goal is to build a frameworkfor detecting and managing relations between ontologies. To do this, an ontology basedapproach is taken and first parts of our framework, focusing on the OSR-Ontology andon the detection mechanisms, is discussed. There are several directions for future work.First, we need to finalize the framework to make it completely automatic, robust andintegrated with WATSON. We also plan to produce a first release of the OSR-Ontologyas well as a more complete evaluation of the detection methods. The versioning prob-lem will require some deeper studies to provide a model and a method to keep track ofversions of the ontologies. Other kinds of ontology relations should also be consideredsuch as incompatibility. Finally, an important aspect is to formalize the model under-lying the linked ontologies in order to provide reasoning and querying mechanisms, aswell as visualizations for networks of ontologies.References1. M. d’Aquin, E. Motta, and M. Sabou et al. Towards a new generation of semanticweb applications. IEEE Intell. Sys., 23(3), 2008.2. J. David and J. Euzenat. Comparison between ontology distances (preliminaryresults). 7th Int. Semantic Web Conference, ISWC, 2008.3. N. Shadbolt H. Alani, C. Brewster. Ranking ontologies with aktiverank. Proc ofthe 4th Int Sem Web Conf (ISWC2006), 2006, LNCS, Springer, pages 1–15., 2006.4. J. Hartmann, R. Palma, and et al. Ontology metadata vocabulary and applications.pages 906–915, OCT 2005.5. J. Heflin and Z. Pan. A model theoretic semantics for ontology versioning. 3thIntern Sem Web Conf, Hiroshima, Japan, LNCS 3298 Springer, pages 62–76., 2004.6. Z. Huang and H. Stuckenschmidt. Reasoning with multi-version ontologies: a tem-poral logic approach. Proc of the Fourth Intern Sem Web Conf (ISWC2005),3729,LNCS, Springer, pages 62–76., 2005.7. M. Klein, D. Fensel, A. Kiryakov, and D. Ognyanov. Ontology versioning andchange detection on the web. 13th Intern Conf on Know. Engineering and Know.Management (EKAW02), pages 197–212, 2002.8. A. Kleshchev and I. Artemjeva. An analysis of some relations among domainontologies. Int. Journal on Inf. Theories and Appl, 12:85–93, 2005.9. N. F. Noy and M. A. Musen. Promptdiff: A fixed-point algorithm for comparingontology versions. 18th National Conf. on Artificial Intelligence (AAAI), 2002.10. M. Volkel. D2.3.3.v2 SemVersion Versioning RDF and Ontologies. EU-IST Net-work of Excellence (NoE) IST-2004-507482 KWEB.

Expliciting semantic relations between ontologies in large ontology repositories

Carlo Allocca

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Expliciting semantic relations between ontologies in large ontology repositories

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