Ontology Partitioning for Ontology Alignment
The main objective of the MSc project is to extend the state of the art in ontology alignment with advanced techniques to compute custom ontology partitions for ontology alignment.
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  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.
Ontology partitioning is a well known technique to enhance alignment of very large ontologies and enable parallel computing (e.g., [3-5]) and potentially distributed user assessment over different partitions. Nonetheless, most partitioning algorithms only focus on one of the input ontologies and neglect the creation of partitions involving the ontology to be aligned to. This has a negative effect in the subsequent (automatic or manual) ontology alignment task since the discovered partitions in one ontology will most likely need to be matched to more than one partition in the other ontology. In this MSc we aim at designing a partitioning algorithm to take into account the ontology alignment task at hand.
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  is an annual campaign for the systematic evaluation of Ontology Alignment systems.
 Pavel Shvaiko, Jérôme Euzenat: Ontology Matching: State of the Art and Future Challenges. IEEE Trans. Knowl. Data Eng. 25(1): 158-176 (2013)
 Manel Achichi, et al.: Results of the Ontology Alignment Evaluation Initiative 2016. OM@ISWC 2016: 73-129
 Faycal Hamdi, Brigitte Safar, Chantal Reynaud, Haifa Zargayouna. Alignment-based Partitioning of Large-scale Ontologies. Fabrice Guillet and Gilbert Ritschard and Djamel Zighed and Henri Briand. Advances in Knowledge Discovery And Management, 292, Springer, pp.251-269, 2010
 Alsayed Algergawy, Friederike Klan, and Birgitta Konig-Ries: Partitioning-based Ontology Matching Approaches: A Comparative Analysis, Ontology Matching workshop, 2014
 Tiago Brasileiro Araújo, Carlos Eduardo Santos Pires, Thiago Pereira da Nóbrega, Dimas C. Nascimento: A fine-grained load balancing technique for improving partition-parallel-based ontology matching approaches. 17-26