One-to-one correspondences are not always sufficient to accurately align ontologies, and instead complex correspondences with conditions and transformations may be required. Correspondence patterns provide models which can be used to guide the process of developing complex correspondences. However, it is necessary to first identify which pattern to apply to a given alignment problem. This PhD proposes the development of algorithms, methods and processes for refining elementary correspondences between concepts or relations into complex ones by identifying which correspondence pattern best represents a given correspondence. To date an evaluation of a system to refine correspondences between classes in the YAGO and DBpedia ontologies has been completed. This evaluation showed that for a subsumption correspondence, a training set of 30 instances of the class being mapped was sufficient to refine the match to a conditional one 89% of the time. Hence we have shown that this is a promising approach for correspondences with a conditional element, and correspondences with a translation element will be examined next.
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