Abstract: Ontology alignment has been an active research topic for over a decade. Over that time, many developers have focused on creating alignment systems and methods to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. There are several reasons for this limitation. First, there are no widely accepted alignment benchmarks that contain such complex relationships. Second, the traditional evaluation metrics like precision, recall, and f-measure are not accurate enough to evaluate the performance of a complex alignment system. And third, the approaches most commonly used to find simple equivalences do not handle the increased computational complexity of finding complex equivalences well. Therefore, it becomes a big challenge for many developers to create and evaluate the systems. In this paper, in order to advance the development of ontology matching, we seek to address the problem by first developing potential complex alignment benchmarks from real-world ontologies. In addition, we utilize traditional automated alignment systems to suggest complex correspondences, and finally plan to achieve our ultimate goal of creating and evaluating our own complex alignment system based on logical RDF data compression.
Keywords: Ontology Alignment; Complex Alignment; Logical RDF compression; Benchmark