Author(s): William Van Woensel, Syed Sibte Raza Abidi
Abstract: Decision support systems, with production rule systems at their core, have an opportunity to leverage the embedded semantics of semantic, ontology-based data to improve decision support accuracy. Advances in mobile hardware are enabling these rule-based systems to be deployed on mobile, ubiquitous platforms. By deploying reasoning processes locally, time-sensitive tasks are no longer influenced by network conditions, less bandwidth is wasted, and less re-mote (costly) resources are needed. Despite hardware advances however, recent benchmarks found that, when directly re-using existing (PC- or server-based) technologies, the scalability of reasoning on mobile platforms is greatly limited. To realize efficient semantic reasoning on resource-constrained platforms, utilizing rule-based axiomatizations of ontology semantics (e.g., OWL 2 RL), which are known to trade expressivity for scalability, is a useful first step. Furthermore, the highly dynamic nature of mobile and ubiquitous settings, where data is typically encountered on-the-fly, requires special consideration. We pro-pose a tailored version of the RETE algorithm, the mainstay algorithm for production rule systems. This algorithm dynamically adapts RETE networks based on the evolving relevance of rules, with the goal of reducing their resource consumption. We perform an evaluation of semantic reasoning using our custom algorithm and an OWL2 RL ruleset, both on the PC and mobile platform.
Keywords: RETE; OWL2 RL; rule-based reasoning; OWL reasoning; reasoning optimization