In this paper we present a preliminary logic-based evaluation of the integration of post-composed phenotypic descriptions with domain ontologies. The evaluation has been performed using a description logic reasoner together with scalable techniques: ontology modularization and approximations of the logical difference between ontologies

Berlanga, R.

Grau, B. C.

Jimenez-Ruiz, E.

Rebholz-Schuhmann, D.

City Research Online

              City, University of London Institutional RepositoryCitation: Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599, Grau, B. C., Berlanga, R. and Rebholz-Schuhmann, D. (2011). First steps in the logic-based assessment of post-composed phenotypic descriptions. CEUR Workshop Proceedings, 698, This is the published version of the paper. This version of the publication may differ from the final published version. Permanent repository link:  http://openaccess.city.ac.uk/id/eprint/22939/Link to published version: Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to.City Research Online:            http://openaccess.city.ac.uk/            publications@city.ac.ukCity Research OnlineFirst steps in the logic-based assessment ofpost-composed phenotypic descriptionsE. Jime´nez-Ruiz1,3, B. Cuenca Grau2, R. Berlanga1, and DietrichRebholz-Schuhmann31 Universitat Jaume I de Castello´, Spain {ejimenez,berlanga}@uji.es2 University of Oxford, UK {berg}@comlab.ox.ac.uk3 European Bioinformatics Institute, Cambridge, UK {jimenez,rebholz}@ebi.ac.ukAbstract. In this paper we present a preliminary logic-based evaluationof the integration of post-composed phenotypic descriptions with domainontologies. The evaluation has been performed using a description logicreasoner together with scalable techniques: ontology modularization andapproximations of the logical difference between ontologies.1 IntroductionA phenotype is defined as a basic observable characteristic of an organism. Thus,a set of phenotypic descriptions may involve different domains and granularitiesranging from molecular to organism level.Phenotypic descriptions have been recently described by means of termi-nological resources, with the Human Phenotype Ontology (HPO) [1] being aprominent example. The HPO ontology represents a so-called pre-composed de-scription: it does not provide explicit links between the phenotypic descriptions(e.g. increased calcium concentration in blood) and the relevant entities asso-ciated to it, such as the chemical element involved (“calcium”), the way inwhich it is involved (“increased concentration”) and where it appears (“blood”).Post-composed phenotypic descriptions intend to provide a more formal repre-sentation to interoperate with involved entities [2] and to allow more powerfulreasoning. Nevertheless, the formal representation of phenotypic descriptions isstill a challenge [3, 4] owing to the complex nature of some phenotypes and thelack of consensus among clinicians to describe them in a standard way.Mungall et al. [3] and Hoehndorf et al. [4] have recently proposed automaticand semi-automatic methods to transform pre-composed phenotypic descrip-tions into a description logic (DL) based post-composed representation linkedto domain ontologies. The integration of domain ontologies with post-composedphenotypic descriptions presents new challenges since most of the involved on-tologies are developed independently and may perform a different conceptual-ization for the same entities. Therefore, this integration may not always lead tothe expected and proper logical consequences [5, 6]. In this paper we presentfirst steps towards the logic-based assessment of the integration of phenotypicdescriptions with domain ontologies.arXiv:1012.1659v1  [cs.AI]  8 Dec 2010Fig. 1. An excerpt from the post-composed phenotypic descriptions of HPOPC2 Method and preliminary resultsOur experiments have been based on a post-composed version (from now onHPOPC) of the HPO ontology4 applying the method from [3]. The HPO ontologyonly provides a classification of pre-composed phenotypic descriptions (e.g. seeleft hand side of Figure 1), whereas HPOPC also provides explicit links to relevantdomain entities (see right hand side of Figure 1). HPOPC contains 11382 entitiesand uses external concepts from different domain ontologies, including PATO [7](264 concepts), Cell Ontology (12 conc.), GO (96 conc.), FMA [8] (812 conc.),CHEBI (33 conc.), and other OBO foundry ontologies [9].A DL reasoner may be used to reclassify HPO concepts, according to theknowledge of HPOPC and linked ontologies, and get new interesting knowledge.However, as stated in [3], reasoning with HPOPC and all linked ontologies is timeconsuming. To smooth this limitation, we have extracted a locality-based module[10] for each set of referenced external entities. For example, the module for FMAcontains 2044 concepts, which is much easier to reason with than the whole FMA(around 80000 concepts). Thus, we have built HPOALL5 by merging HPOPC withthe corresponding modules from the referenced ontologies. The classification ofHPOALL using HermiT [12] takes around 45 seconds in a 2Gb laptop.New subsumption relationships between HPO concepts may represent bothdesired new knowledge and unintended consequences. In order to evaluate thenew logical consequences hold in HPOALL we have borrowed the notion of logicaldifference from [13]. The logical difference between two ontologies contains theset of consequences that are inferred in one of the ontologies but not in theother. Unfortunately, there is no algorithm for computing the logical differencein expressive DLs. Moreover, the number of inferences in the difference maybe infinite. Thus, we have reused the approximations of the logical differencepresented in previous work [5], where inferences are one of the following simplekinds of axiom: (i) A v B, (ii) A v ¬B, (iii) A v ∃R.B, (iv) A v ∀R.B, andv) R v S (A,B are atomic concepts, including >,⊥, and R,S atomic roles).4 Available from http://bioonto.de/obo2owl/hpo-in-owl.owl5 We have converted the OBO ontologies to OWL using the OWLDEF method [11],and we have normalized the involved concept and property URIsFig. 2. Explanation for new equivalence between concepts HP 0000969 (Edema) andHP 0007430 (Generalized edema). With concept IDs (left) and concept names (right).Fig. 3. Explanation for new subsumption relationship between concepts GO 0008544(Epidermis development) and FMA 67175 (Anatomical entity)The logical difference between HPOALL and HPOPC, affecting only HPO con-cepts, contains 759 new subsumption relationships (inferences of type (i)). Theintegration leads indeed to a reclassification of HPO concepts. For example,HPOALL infers the probably non-intended consequence Generalized edema ≡Edema which was not hold in HPOPC. As shown in the Prote´ge´-like explanationfrom Figure 2 the new knowledge from FMA leads to this new consequence.The logical difference also contains 80 new entailments that relate conceptsfrom domain ontologies (i.e. new cross-references). For example, the GO conceptEpidermis development is classified under the FMA concept Anatomical entity.This consequence is probably not intended and it is due to the definition ofrange axioms in FMA (see Figure 3) and the use of the property part of indifferent scopes (in FMA relates anatomical entities, whereas in GO biologicalprocesses). Additionally, if a greater approximation of the logic difference is con-sidered (i.e. entailments of type (ii)-(v)) new consequences are also obtained (e.g.GO 0030308 v ∃negatively regulates.GO 0040007, where GO 0030308 standsfor Negative regulation of cell growth and GO 0040007 stands for Growth.3 Conclusions and future workThe benefits of integrating phenotypic descriptions with domain ontologies havealready discussed in the literature [2, 3, 4]. However, the consequences of theintegration should be evaluated by domain experts in order to detect potentialunintended consequences.In this paper we have performed a preliminary evaluation6 in which stateof the art techniques (e.g. ontology reasoning, ontology modularization, logicaldifference) have been reused to extract the set of new consequences when inte-grating post-composed phenotypic descriptions, such as the provided by HPOPC,with domain ontologies. In a near future, we intend to develop a system to guidethe expert in the detection and repair of unintended consequences such as in ourprevious tool ContentMap [5], in which we assessed the integration of ontologiesthrough mappings.Moreover, domain ontologies contains cross-references (i.e. mappings) whichhave not been considered for this preliminary assessment. These new correspon-dences will probably lead to new consequences that should be assessed. Thus,we also intend to adapt the techniques proposed in [6] to this new setting.References[1] Robinson, P., Mundlos, S.: The Human Phenotype Ontology. Clinical Genetics77(6) (2010) 525–534[2] Lussier, Y.A., Li, J.: Terminological mapping for high throughput comparativebiology of phenotypes. In: Pacific Symposium on Biocomputing. (2004) 202–213[3] Mungall, C., Gkoutos, G., Smith, C., Haendel, M., Lewis, S., Ashburner, M.:Integrating phenotype ontologies across multiple species. Genome Biology 11(1)(2010) R2[4] Hoehndorf, R., Oellrich, A., Rebholz-Schuhmann, D.: Interoperability betweenphenotype and anatomy ontologies. Bioinformatics (2010)[5] Jime´nez-Ruiz, E., Cuenca Grau, B., Horrocks, I., Berlanga, R.: Ontology integra-tion using mappings: Towards getting the right logical consequences. In: EuropeanSemantic Web Conference. Volume 5554 of LNCS. (2009) 173–187[6] Jime´nez-Ruiz, E., Cuenca Grau, B., Horrocks, I., Berlanga, R.: Logic-based as-sessment of the compatibility of UMLS ontology sources. Accepted for publicationin Journal of Biomedical Semantics (2010)[7] Gkoutos, G., Green, E., Mallon, A.M., Hancock, J., Davidson, D.: Using ontologiesto describe mouse phenotypes. Genome Biology 6(1) (2004) R8[8] Mejino Jr., J.L.V., Rosse, C.: Symbolic modeling of structural relationships inthe foundational model of anatomy. In: Proceedings of KR-MED. (2004) 48–62[9] Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg,L.J., et al.: The OBO foundry: coordinated evolution of ontologies to supportbiomedical data integration. Nature biotechnology 25(11) (2007) 1251–1255[10] Cuenca Grau, B., Horrocks, I., Kazakov, Y., Sattler, U.: Modular reuse of ontolo-gies: Theory and practice. J. of Artificial Intelligence Research 31 (2008)[11] Hoehndorf, R., Oellrich, A., Dumontier, M., Kelso, J., Rebholz-Schuhmann, D.,Herre, H.: Relations as patterns: bridging the gap between OBO and OWL. BMCBioinformatics 11(1) (2010) 441[12] Motik, B., Shearer, R., Horrocks, I.: Hypertableau Reasoning for DescriptionLogics. Journal of Artificial Intelligence Research 36 (2009) 165–228[13] Konev, B., Walther, D., Wolter, F.: The logical difference problem for descriptionlogic terminologies. In: IJCAR. Volume 5195 of LNCS., Springer (2008) 259–2746 HPOALL and related domain ontology modules are available at:http://krono.act.uji.es/people/Ernesto/phenotypeassessment/

First steps in the logic-based assessment of post-composed phenotypic descriptions

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First steps in the logic-based assessment of post-composed phenotypic descriptions

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