Optimizing Semantic Reasoning on Memory-Constrained Platforms Using the RETE Algorithm

Abstract

Mobile hardware improvements have opened the door for deploying rule systems on ubiquitous, mobile platforms. By executing rule-based tasks locally, less remote (cloud) resources are needed, bandwidth usage is reduced, and local, time-sensitive tasks are no longer influenced by network conditions. Further, with data being increasingly published in semantic format, an opportunity arises for rule systems to leverage the embedded semantics of semantic, ontology-based data. To support this kind of ontology-based reasoning in rule systems, rule-based axiomatizations of ontology semantics can be utilized (e.g., OWL 2 RL). Nonetheless, recent benchmarks have found that any kind of semantic reasoning on mobile platforms still lacks scalability, at least when directly re-using existing (PC- or server-based) technologies. To create a tailored solution for resource-constrained platforms, we propose changes to RETE, the mainstay algorithm for production rule systems. In particular, we present an adapted algorithm that, by selectively pooling RETE memories, aims to better balance memory usage with performance. We show that this algorithm is well-suited towards many typical Semantic Web scenarios. Using our custom algorithm, we perform an extensive evaluation of semantic, ontology-based reasoning, using our custom RETE algorithm and an OWL2 RL ruleset, both on the PC and mobile platform.