# Rule Applicability on RDF Triplestore Schemas

## TL;DR

This paper introduces methods to determine rule applicability on RDF triplestore schemas, crucial for efficient reasoning in large, dynamic datasets like IoT, by proposing a novel query rewriting approach with proven efficiency.

## Contribution

It presents a new query rewriting method for assessing rule applicability on RDF schemas, improving efficiency over existing canonical instance approaches.

## Key findings

- Rewriting approach outperforms canonical instance method in efficiency
- Theoretical analysis confirms the correctness of the rewriting approach
- Evaluation demonstrates practical applicability in large-scale RDF datasets

## Abstract

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.