Java, Translators, Semantic Similarity Measures, Semantic Web Technologies: OWL, RDF, Jena, SPARQL
Ontologies are adopted by various applications in diverse domains, such as, biomedical, geo spatial or legal data, 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, in the context of Web information retrieval or social networks, semantic interoperability is also hampered by the conceptual diversity of used vocabularies and various domain requirements (Euzenat et al., 2013).
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. These semantic correspondences bridge indeed heterogeneous ontologies together and ensure their semantic interoperability. 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 OAEI evaluation campaign (ontology Alignment Evaluation Initiative http://oaei.ontologymatching.org/ where this contest is organized each year and different competitors participate with their systems in order to evaluate the best system against different Benchmarks (ontologies).
The matching is not a trivial task for mono-language ontologies due to conceptual diversity. This task, however, is even more challenging for multilingual ontologies. Thus, the multilingualism has become a major interest in the ontology matching field, especially with the growing number of multilingual ontologies, and, several ontology matching systems are developed to establish mappings between multilingual ontologies. Most of these systems use a translator (to English as a pivot) to deal with multilingual ontologies (Khiat, 2016) in order to discover mappings.
- Establishing an overview of translation machines used for aligning multilingual ontologies;
- Seeking for new approach to deal with multilingualism or enhance the translation algorithm in order to discover more mappings than traditional approaches.
R. Neches, R. E. Fikes, T. Finin, T. Gruber, R. Patil, T. Senator, and W. R. Swartout. Enabling technology for knowledge sharing. AI magazine, 12(3):36, 1991.
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.
- A. Khiat. Crolom: cross-lingual ontology matching system results for OAEI 2016. In Proceedings of the 11th International Workshop on Ontology Matching co-located with the 15th International Semantic Web Conference (ISWC 2016), Kobe, Japan, October 18, 2016., pages 146–152, 2016.