On Computing Deltas of RDF/S Knowledge Bases

Abstract

The ability to compute the differences that exist between two RDF/S Knowledge Bases (KB) is an important step to cope with the evolving nature of the Semantic Web (SW). In particular, RDF/S deltas can be employed to reduce the amount of data that need to be exchanged and managed over the network in order to build SW synchronization and versioning services. By considering deltas as sets of change operations, in this article we introduce various RDF/S differential functions which take into account inferred knowledge from an RDF/S knowledge base. We first study their correctness in transforming a source to a target RDF/S knowledge base in conjunction with the semantics of the employed change operations (i.e., with or without side-effects on inferred knowledge). Then we formally analyze desired properties of RDF/S deltas such as size minimality, semantic identity, redundancy elimination, reversibility, and composability, as well as identify those RDF/S differential functions that satisfy them. Subsequently, we experimentally evaluate the computing time and size of the produced deltas over real and synthetic RDF/S knowledge bases.