Building an effective and efficient background knowledge resource to enhance ontology matching

Date: April 27, 2018

Speaker: Amina Annane

Abstract

Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources (BK) [1].

Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. We proposed an approach to build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness [2]. In our talk, we will discuss the BK-based ontology matching and present our contributions to this research area.

Bio

Amina ANNANE is a Ph. D. student at Laboratory of Informatics, Robotics, and Microelectronics of Montpellier, France (LIRMM) && Higher National School of Computer Science, Algeria (ESI). Her research focuses on improving the matching of biomedical ontologies using external knowledge resources.

She obtained an engineering degree and a master degree in computer science in 2013 from the Higher National School of Computer Science, Algeria (ESI).

E-mail: amina.annane at lirmm.fr

References

1. Sabou, M., d’Aquin, M., & Motta, E. (2008). Exploring the semantic web as background knowledge for ontology matching. In Journal on data semantics XI (pp. 156-190). Springer Berlin Heidelberg.
2. Annane, A., Bellahsene, Z., Azouaou, F., & Jonquet, C. Selection and Combination of Heterogeneous Mappings to Enhance Biomedical Ontology Matching. 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW) 2016. pp:19-33. Springer.