High-Level Change Detection in RDF(S) KBs

With the increasing use of Web 2.0 to create, disseminate, and consume large volumes of data, more and more information is published and becomes available for potential data consumers, that is, applications/services, individual users and communities, outside their production site. The most representative example of this trend is Linked Open Data (LOD), a set of interlinked data and knowledge bases. The main challenge in this context is data governance within loosely coordinated organizations that are publishing added-value interlinked data on the Web, bringing together issues related to data management and data quality, in order to support the full lifecycle of data production, consumption, and management. In this article, we are interested in curation issues for RDF(S) data, which is the default data model for LOD. In particular, we are addressing change management for RDF(S) data maintained by large communities (scientists, librarians, etc.) which act as curators to ensure high quality of data. Such curated Knowledge Bases (KBs) are constantly evolving for various reasons, such as the inclusion of new experimental evidence or observations, or the correction of erroneous conceptualizations. Managing such changes poses several research problems, including the problem of detecting the changes (delta) between versions of the same KB developed and maintained by different groups of curators, a crucial task for assisting them in understanding the involved changes. This becomes all the more important as curated KBs are interconnected (through copying or referencing) and thus changes need to be propagated from one KB to another either within or across communities. This article addresses this problem by proposing a change language which allows the formulation of concise and intuitive deltas. The language is expressive enough to describe unambiguously any possible change encountered in curated KBs expressed in RDF(S), and can be efficiently and deterministically detected in an automated way. Moreover, we devise a change detection algorithm which is sound and complete with respect to the aforementioned language, and study appropriate semantics for executing the deltas expressed in our language in order to move backwards and forwards in a multiversion repository, using only the corresponding deltas. Finally, we evaluate through experiments the effectiveness and efficiency of our algorithms using real ontologies from the cultural, bioinformatics, and entertainment domains.