Finding Statistically Significant Interactions between Continuous Features

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.Comment: 13 pages, 5 figures, 2 tables, accepted to the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019

Graph-based discovery of ontology change patterns

Ontologies can support a variety of purposes, ranging from capturing conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. We investigate ontology change representation and discovery of change patterns. Ontology changes are formalised as graph-based change logs. We use attributed graphs, which are typed over a generic graph with node and edge attribution.We analyse ontology change logs, represented as graphs, and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usagedriven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental result