Discovering Mappings between Large-Scale Biomedical/Biodiversity Ontologies

Academic Advisor: Dr. Abderrahmane Khiat , Dr. Naouel Karam
Discipline: Ontology Matching
Degree: Master of Science (M.Sc.)
Project:

Requirements:

Java; Semantic Web Technologies: OWL, RDF, Jena, SPARQL; Semantic Similarity Measures; Indexation Techniques; Ontology Repository

Contents

The work will take place in the context of the GFBio Terminology Service (TS) https://terminologies.gfbio.org/, a repository and services for semantic enrichment of research data in the domain of ecology and biodiversity. The TS contains a set of ontologies locally stored in a Semantic Web repository, in order to make those ontologies more useful for the community, there is a need for interlinking them.

Ontologies are adopted by various applications in diverse domains, such as, biomedical, geo spatial to describe their content and express the semantics of information, since they play an important role in achieving semantic interoperability (Neches  et al. 1991). However, these ontologies are themselves distributed and heterogeneous.
A promising solution to this vocabulary heterogeneity is to apply an alignment (or matching) process to build a bridge between ontologies/linked data to close the semantic gap (Euzenat et al., 2013). Ontology alignment is defined as the process of identification of semantic correspondences (Concepts and Relations/Instances) between entities of different ontologies. The automatic identification of semantic correspondences is not a trivial task due to conceptual diversity (Bouquet et al., 2005) at various levels; terminological, structural, conceptual, etc. 

For instance, if we take the following mapping: the concept  "Automobile" of ontology O1 is equivalent to the concept "Car" of ontology O2, however, this mapping could not be detected using string based algorithm, an alternative solution is to use a dictionary or the structure of the ontology, since they have the same type "Vehicle". Using the structure is not an absolute solution because if we take "Cat" and "Dog"  which have the same type "Animal" but in "anatomy domain" they are considered as two different concepts because they have different anatomical structure.

If the Master student is up to the challenges, their designed systems should compete in the OAEI evaluation campaign (ontology Alignment Evaluation Initiative http://oaei.ontologymatching.org/). This contest is organized each year and different competitors participate with their systems in order to evaluate the best system against different Benchmarks (ontologies).

Objectives

  • Establishing an overview of the systems used for aligning large-scale ontologies such as LogMap (Jiménez-Ruiz et al 2011) or AML (Faria et al., 2013) systems; especially for the ontologies that have been developed in bio domain and the (contain more than 10 000 concepts (see bioportal platform https://bioportal.bioontology.org/)).

  • Proposing and implementing new techniques for matching the GFBio TS set of ontologies, that comprise both the discovering of mappings (Euzenat et al., 2013) and a high performance of similarity computation (Algergawy et al, 2014) (Algergawy et al, 2014) .

References

  • R. Neches, R. E. Fikes, T. Finin, T. Gruber, R. Patil, T. Senator, andW. R. Swartout. Enabling technology for knowledge sharing. AI magazine, 12(3):36, 1991.

  • S. s. A. Guangyuan Piao and J. G. Breslin. Computing the semantic similarity of resources in dbpedia for recommendation purposes. 2015.

  • P. Bouquet, M. Ehrig, J. Euzenat, E. Franconi, P. Hitzler, M. Krotzsch, L. Serafini, G. Stamu, Y. Sure, and S. Tessaris. Specification of a common framework for characterizing alignment.2005.

  •  J. Euzenat, P. Shvaiko, Ontology matching, volume 18. Springer, 2013.

  • F. K. Alsayed Algergawy and B. Konig-Ries. Partitioning-based ontology matching approaches A comparative analysis. 2014.

  • C. R. H. Z. Fayal Hamdi, Brigitte Safar. Alignment-based partitioning of large-scale ontologies. 2009.

  • D. Faria, C. Pesquita, E. Santos, M. Palmonari, I. F. Cruz, F. M. Couto: The AgreementMakerLight Ontology Matching System. OTM Conferences 2013: 527-541

  • Ernesto Jiménez-Ruiz, Bernardo Cuenca Grau: LogMap: Logic-Based and Scalable Ontology Matching. International Semantic Web Conference (1) 2011: 273-288

Websites

http://oaei.ontologymatching.org/