Towards Association RuleBased Complex Ontology Alignment Ontology alignment has been studied for over a decade, and over that time many alignment systems have been developed by researchers in order to find simple 1to1 equivalence alignments between ontologies. However, finding complex alignments, i.e., alignments that are not simple class or property equivalences, is a topic largely unexplored but with growing significance. Currently, establishing a complex alignment requires domain experts to work together to manually generate the alignment, which is extremely timeconsuming and laborintensive. In this paper, we propose an automated method based on association rule mining to detect not only simple alignments, but also more complex alignments between ontologies. Our algorithm can also be used in a semiautomated fashion to effectively assist users in finding potential complex alignments which they can then validate or edit. In addition, we evaluate the performance of our algorithm on the complex alignment benchmark of the Ontology Alignment Evaluation Initiative (OAEI).

Mining Association Rules From Disease Ontology The Disease Ontology (DO) is standardized, controlled vocabulary that contains information about inherited, developmental and acquired human diseases. Each DO term is associated with disease concepts through an annotation process. The relevance and the specificity of DO terms are often evaluated by its Information Content (IC). An important research area focus on the analysis of annotated data with the goal to extract knowledge. For example, the analysis of annotated data using Association Rules (AR) may supply meaningful knowledge, discovering relevant associations. Classical association rules methods consider all annotation equally, do not taking into account that the DO terms have different Information Content, i.e. different relevance. This implies the generation of association rules with low IC. In this paper we presents WARDO (Weighted Association Rule mining from Disease Ontology), a methodology based on the extraction od Weighted Association Rules from the DO Ontology considering the IC of terms. To assess our methodology, we tested WARDO on DO annotation datasets. WARDO is publicly available at https:gitlab.comgiuseppeagapitowardo.