Ph.D. Dissertation Proposal Defense Towards Complex Ontology Alignment By Lu Zhou

Ph.D. Committee:  Drs. Pascal Hitzler (advisor), Michelle Cheatham, TK Prasad, and Catia Pesquita (University of Lisbon)

ABSTRACT:

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order 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, tackling complex alignment is a big challenge that requires experts from different domains to work together to manually generate the alignment, which is extremely time-consuming. Third, the traditional evaluation metrics like precision, recall, and f-measure are not fine-grained enough to evaluate the performance of a complex alignment system. Therefore, it becomes a big challenge for many developers to create and evaluate the systems. In this work, 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. The benchmark consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment, that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. Then, we create an automated complex alignment system based on association rule mining to generate complex correspondences. Our algorithm can also be used in a semi-automated fashion to effectively assist users in finding potential complex alignment which they can then validate or edit. In addition, we evaluate the performance of our algorithm on our benchmark and analyze the results in detail to provide the insights of the field in the complex ontology alignment.