Time - Space Trade-Offs in Scaling up RDF Schema Reasoning

Abstract

Abstract. A common way of reducing run time complexity of RDF Schema reasoning is to compute (parts of) the deductive closure of a model offline. This reduces the complexity at run time, but increases the space requirements and model maintenance because derivable facts have to be stored explicitly and checked for validity when the model is updated. In this paper we experimentally identify certain kinds of statements as the major sources for the increase. Based on this observation, we develop a new approach for RDF reasoning that only computes a small part of the implied statements offline thereby reducing space requirements, upload time and maintenance overhead. The computed fragment is chosen in such a way that the problem of inferring implied statements at run time can be reduced to a simple form of query re-writing. This new methods has two benefits: it reduces the amount of storage space needed and it allows to perform online reasoning without using a dedicated inference engine. A common way of reducing run time complexity of RDF Schema reasoning is to compute (parts of) the deductive closure of a model offline using the deduction rule