Benchmarking semantic reasoning on mobile platforms: Towards optimization using OWL2 RL

Abstract

Mobile hardware has advanced to a point where apps may consume the Semantic Web of Data, as exemplified in domains such as mobile context-awareness, m-Health, m-Tourism and augmented reality. However, recent work shows that the performance of ontology-based reasoning, an essential Semantic Web building block, still leaves much to be desired on mobile platforms. This presents a clear need to provide developers with the ability to benchmark mobile reasoning performance, based on their particular application scenarios, i.e., including reasoning tasks, process flows and datasets, to establish the feasibility of mobile deployment. In this regard, we present a mobile benchmark framework called MobiBench to help developers to benchmark semantic reasoners on mobile platforms. To realize efficient mobile, ontology-based reasoning, OWL2 RL is a promising solution since it (a) trades expressivity for scalability, which is important on resource-constrained platforms; and (b) provides unique opportunities for optimization due to its rule-based axiomatization. In this vein, we propose selections of OWL2 RL rule subsets for optimization purposes, based on several orthogonal dimensions. We extended MobiBench to support OWL2 RL and the proposed ruleset selections, and benchmarked multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Our results show significant performance improvements by applying OWL2 RL rule subsets, allowing performant reasoning for small datasets on mobile systems.

Tiny-UKSIE

“…The Two-phase RETE algorithm includes a most specific condition first and a pre-evaluation of join connectivity to diminish the RETE network. The study in (Van Woensel, 2019) proposed an OWL2 RL optimization including leaving out redundant rules, dividing the rule set based on rule purpose and references, and removing resource-heavy rules to reduce the size of the Beta network. The researchers in (Xin, 2017) propose an improved RETE algorithm using a shared degree model.…”