Project Background and Scientific Basis
Ontologies are extensively used in biology and medicine. Ontologies such as SNOMED CT, the National Cancer Institute Thesaurus (NCI), and the Foundational Model of Anatomy (FMA) are gradually superseding existing medical classifications and are becoming core platforms for accessing, gathering and sharing bio-medical knowledge and data. These reference biomedical ontologies, however, are being developed independently by different groups of experts and, as a result, they use different entity naming schemes and modelling conventions. As a consequence, to integrate and migrate data among applications, it is crucial to first establish correspondences (or
mappings) between the vocabularies of their respective ontologies.
In the last ten years, the Semantic Web and biomedical research communities have extensively investigated the problem of automatically computing mappings between independently developed ontologies, usually referred to as the ontology matching problem (see [1] for a comprehensive and up-to-date survey). The growing number of available techniques and increasingly mature tools, together with substantial human curation effort and complex auditing protocols, has made the generation of mappings between real-world ontologies possible.
The alignment of large ontologies including large datasets still poses important challenges to the state-of-the-art systems in ontology alignment. In this MSc project we aim designing and implementing techniques to cope with large ontologies and large datasets.
Supervision
The thesis will be jointly supervised by Dr. Ernesto Jimenez-Ruiz and Prof. Martin Giese from the Logic and Intelligent Data (LogID) group, based in the Department of Informatics.
The LogID group is also actively contributing to the Ontology Matching community and (co)organises the annual Ontology Alignment Evaluation Initiative (OAEI). The OAEI [2] is an annual campaign for the systematic evaluation of Ontology Alignment systems.
References
[1] Pavel Shvaiko, Jérôme Euzenat: Ontology Matching: State of the Art and Future Challenges. IEEE Trans. Knowl. Data Eng. 25(1): 158-176 (2013)
[2] Manel Achichi, et al.: Results of the Ontology Alignment Evaluation Initiative 2016. OM@ISWC 2016: 73-129