Formalizing High-Level Change Detection for RDF / S KBs

An increasing number of scientific communities rely on Seman tic Web ontologies to share and interpret data within and across research domains. These common knowledge representation resources are usual ly developed and maintained manually and essentially co-evolve along with e xperimental evidence produced by scientists worldwide. Detecting automaticall y the differences between (two) versions of the same ontology in order to store or visualize their deltas is a challenging task for e-science. In this paper, we focus on languages allowing the formulation of concise and intuitive deltas, w hich are expressive enough to describe unambiguously any possible change and th t can be effectively and efficiently detected. We propose a specific langua ge that provably exhibits those characteristics and provide a change detectio n algorithm which is sound and complete with respect to the proposed language. Fi nally, we provide a promising experimental evaluation of our framework using r eal ontologies from the cultural and bioinformatics domains.