Creating a new Ontology: a Modular ApproachComment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott
  Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on
  Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany,
  December 8-10, 201

Dmitrieva, Julia

Verbeek, Fons J.

English

arXiv

Dmitrieva, J.

Verbeek, F.J.

Leiden University Scholary Publications

Creating a new ontology: a modular approachDmitrieva, J.; Verbeek, F.J.; Burger, A.; Marshall, M.S.; Romano, P.; Paschke, A.;Splendiani, A.CitationDmitrieva, J., & Verbeek, F. J. (2010). Creating a new ontology: a modular approach.Proceedings Of The Workshop On Semantic Web Applications And Tools For Life Sciences,1-4. Retrieved from https://hdl.handle.net/1887/3640117 Version: Publisher's VersionLicense: Licensed under Article 25fa Copyright Act/Law (Amendment Taverne)Downloaded from: https://hdl.handle.net/1887/3640117 Note: To cite this publication please use the final published version (if applicable).Creating a new Ontology: a Modular ApproachJulia Dmitrieva and Fons J. VerbeekUniversiteit Leiden, Leiden Institute of Advanced Computer Science,Niels Bohrweg 1, 2333 CA Leiden, The Netherlands{jdmitrie,fverbeek}@liacs.nlhttp://bio-imaging.liacs.nl/Keywords: module extraction, ontology mapping, ontology integration1 IntroductionOntologies in life sciences, in particular, members of the OBO Foundry [6],contain information about species, proteins, chemicals, genomes, pathways, dis-eases, etc. Information in these ontologies might overlap, and it is possible thata certain concept is defined in different ontologies from a different point of viewand at different level of granularity. Therefore, the combination of informationfrom different ontologies is useful to create a new ontology.Case Study The integration will be illustrated with a case study on Toll-likereceptors. If we want to investigate what kind of information about Toll-like re-ceptors is available in Molecule Role Ontology (MoleculeRoleOntlogy) [6],then we will see that Toll-like receptors are defined as pattern recognition recep-tors. In the Biological Process Ontology (GO) [6] the Toll-like receptorsare described in the context of signaling pathway and are subsumed by thepattern recognition receptor signaling pathway. In the Protein [6] ontology aToll-like receptor is just a protein. In the NCI Thesaurus [5] ontology Toll-likereceptors are defined as Cell Surface Receptors. It follows from foregoing thatmultiple ontologies model different aspects of the same concept and the com-bination of the available information provides more knowledge about conceptswhere an ontology developer is interested in.We introduce an approach for generating a new ontology in which ontologiesfrom OBO Foundry are reused. First, we extract modules from these ontolo-gies, on the basis of the well defined modularity approach [2]. As a signaturefor the modules we are using the symbols that match the terms of interest asindicated by the user. In our case study we create an ontology about Toll-likereceptors, therefore we use two seed terms (Toll, TLR). Subsequently, we createmappings between concepts in the modules. It has already been shown [1] thatthe simple similarity algorithms outperform structural similarity algorithms inbiomedical ontologies. To this end, we have based our mappings on the similar-ity distance [4] between labels and synonyms of classes in the modules. Finally,a new ontology is created where the mappings are represented by means ofowl:equivalentClass axiom and small concise modules are imported.2 Creating a new Ontology: a Modular Approach2 Modules from Enriched SignatureIn our case study we have used the following biomedical ontologies obtained fromOBO Foundry: National Cancer Institute Ontology (NCI Thesaurus), GOOntology (GO), Protein Ontology (PRO), Dendritic Cell ontology(dendritic cell), Pathway ontology (pathway), Molecule Role Ontology(MoleculeRoleOntology), Gene Regulation Ontology (gene regulation),and finally, Medical Subject Heading ontology (MeSH). All of these ontologiesare in OBO format, except for the NCI Thesaurus, which is in OWL format.A module comprises knowledge of a part of the domain that is dedicated toa set of terms of user interest (seed terms). Let T1 = {Toll, TLR} be this set.Let S1 be a set of terms (signature) from the ontology O1 that represents theclasses whose labels, descriptions, ID, or other annotation properties contain thesymbols from T1. The first module that we have extracted is the module fromNCI ThesaurusM1. This is chosen because it is the largest ontology containingthe most matches. In order to generate a signature for the next ontology O2, weare using not only the terms from T1 but we enrich this set with the terms fromthe module M1. The same procedure is applied for the rest of the ontologies,namely module Mi is extracted on the basis of the terms Ti = Sig(Mi−1)∪Ti−1.This method has two drawbacks. First, it depends on the order of ontologies.Second, with the generation of the new module Mi new symbols can be intro-duced that will match symbols from ontologies used in previous steps. Theseproblems can be solved with the generation of a fixpoint.Fixpoint Modules We have investigated whether or not we will find a fixpointwith our module extraction method. The fixpoint is reached at the moment theset of terms T which is used in order to generate modules during step ti does notchange any more after another run with all ontologies. This can be written as∪nk=1Sig(Mk,i) = ∪nk=1Sig(Mk,i+1), where Mk,i is the module k created duringstep ti. It can be formulated in a ”fixpoint-like” way Match(T ) = T .The fixpoint was reached with the following sizes of the modules, see Table 1.3 Ontology MappingIn this paper we use a more loosely definition of the concept mapping comparedwith the definition given in [3] in which mapping is a morphism. In our approachmapping is a partial function that maps from subset S1 ⊆ Sig(O1) to subsetS2 ⊆ Sig(O2). We deliberatively reject the morphism requirement, thus, thestructural dependencies will not be preserved after mapping, because we areinterested in consequents of this mapping to the original ontologies, namely,whether and how the structural dependences will be broken.For our experimental prototype system we use our own mappings based onthe syntactic similarity. It has been already shown [1] that in the case of biomed-ical ontologies the simple mappings methods are sufficient and outperform morecomplex methods.Creating a new Ontology: a Modular Approach 3Table 1: The size of the modules after reaching the fixpointmodule size in KBToll from gene regulation 88.7Toll from protein 23.4Toll from chebi 218.6Toll from mesh 59.2Toll from dendritic cell 4.2Toll from pathway 4.1Toll from cellular component 35.4Toll from molecular function 11.4Toll from MoleculeRoleOntology 46.9Toll from biological process 221.1Toll from Thesaurus 802.1We compare characteristics (id, label, description) for all classes from on-tology O1 with the same characteristics for all classes from ontology O2. Thecomparison is based on the Levenshtein distance algorithm [4]. We have adaptedthe Levenshtein distance and introduce a metric Lev (in the range [0 . . . 1]). Twoclasses Ci and Cj are considered to be similar if they have the maximum valuefor Lev metric and if this value is also higher than the threshold t = 0.95 thatwas experimentally determined.4 Integration Information from OntologiesThe final step of the ontology creation is the integration of the modules intoone ontology. If there a mapping exists between two classes Ci and Cj from themodules Mi and Mj respectively we add the equivalence relationowl:equivalentClass between these classes in the new ontology. Besides theequivalence relationships the new ontology contains the OWL:imports axioms,where all the created modules are imported.So far, this all seems rather straightforward. However, the problem withthis integrated ontology O1...n is that it contains many unsatisfiable classes. Inorder to understand the reason of this unsatisfiability we have applied differentexperiments. First, we have merged all pairs of the modules, namely ∀i ￿=jOi,j ≡Mi∪Mj . For each merged ontology Oi,j we have checked for unsatisfiable classes.Already at this stage of integration different merged pairs contain unsatisfiableclasses. We have used the Pellet [7] reasoner in order to reveal the explanationsof unsatisfiability. After we have repaired unsatisfiable classes in the mergedpairs of ontologies Oi,j we have had to check satisfiability of the integratedontology O1...n. There were still 46 unsatisfiable classes. The unsatisfiabilitiesin the integrated ontology have also been solved by means of Pellet reasonerexplanations.4 Creating a new Ontology: a Modular Approach5 ConclusionWe have described a method to generate a new ontology on the basis of thebio-ontologies most of which are available in OBO Foundry. We have shownhow to create modules on the basis of the terms of interest. The signature forthe module extraction is enriched by the symbols from other modules with thefixpoint as a stop criterion. We have integrated modules on the basis of mappingscreated using Levenshtein distance similarity.We have investigated how to solve unsatisfiable classes which appear afterthe integration of the modules. Although the number of unsatisfiable classeswas high, it was possible to solve unsatisfiabilities with the help of explanationsprovided by the Pellet reasoner.In this study we have shown that the modularity and simple mappingsprovide a good foundation for the creation of a new ontology in an pseudo-automated way. This method can be used when an ontology engineer does notwant to create a new ontology from scratch, but rather wants to reuse knowledgealready presented in other ontologies. Moreover, this is the strategy that shouldbe preferred and has to be applied more often as ontologies gain importance inlife sciences.References1. Ghazvinian, A., Noy, N.F., Musen, M.A.: Creating mappings for ontologies inbiomedicine: Simple methods work. In: AMIA 2009 Symposium Proceedings (2009)2. Grau, B.C., Horrocks, I., Kazakov, Y., Sattler, U.: Extracting modules from ontolo-gies: A logic-based approach. In: Modular Ontologies, pp. 159–186 (2009)3. Kalfoglou, Y., Schorlemmer, W.M.: Ontology mapping: The state of the art. In:Semantic Interoperability and Integration (2005)4. Levenshtein, V.: Binary codes capable of correcting, deletions, insertions, and re-versals. Soviet Physics-Doklady 10(8), 845–848 (August 1965)5. NCI: Terminology resources: Nci enterprise vocabulary services (evs), dictio-naries, fedmed, fda, cdisc, and ncpdp terminology. http://www.cancer.gov/cancertopics/terminologyresources6. OBO: The open biomedical ontologies. http://www.obofoundry.org/7. Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical owl-dlreasoner. J. Web Sem. 5(2), 51–53 (2007), http://www.informatik.uni-trier.de/~ley/db/journals/ws/ws5.html#SirinPGKK07

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