Rule Applicability on RDF Triplestore Schemas
This addresses a bottleneck in rule-based systems for domains like health and safety with dynamic IoT data, though it is incremental.
The paper tackles the problem of efficiently determining rule applicability on large RDF datasets by defining triplestore schemas and proposing a method to compute output schemas, with results showing superior efficiency for a novel query rewriting approach.
Rule-based systems play a critical role in health and safety, where policies created by experts are usually formalised as rules. When dealing with increasingly large and dynamic sources of data, as in the case of Internet of Things (IoT) applications, it becomes important not only to efficiently apply rules, but also to reason about their applicability on datasets confined by a certain schema. In this paper we define the notion of a triplestore schema which models a set of RDF graphs. Given a set of rules and such a schema as input we propose a method to determine rule applicability and produce output schemas. Output schemas model the graphs that would be obtained by running the rules on the graph models of the input schema. We present two approaches: one based on computing a canonical (critical) instance of the schema, and a novel approach based on query rewriting. We provide theoretical, complexity and evaluation results that show the superior efficiency of our rewriting approach.