Optimizing and Benchmarking OWL2 RL for Semantic Reasoning on Mobile Platforms

Tracking #: 1666-2878

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William van Woensel
Syed Sibte Raza Abidi

Responsible editor: 
Thomas Lukasiewicz

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The Semantic Web has grown immensely over the last decade, and mobile hardware has advanced to a point where mobile apps may consume this Web of Data. This has been 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. Applying OWL2 RL to realize such mobile reasoning is a promising solution, since it trades expressivity for scalability, and its rule-based axiomatization easily allows applying axiom subsets to improve performance. At any rate, considering the current performance issues, developers should be able to bench-mark reasoners on mobile platforms, using different process flows, reasoning tasks, and datasets. To that end, we developed a mobile benchmark framework called MobiBench. In an effort to optimize mobile ontology-based reasoning, we further propose selections of OWL2 RL rule subsets based on logical equivalence, purpose and reference, and domain relevance. Using MobiBench, we benchmark multiple OWL2 RL-enabled rule engines and OWL reasoners on a mobile platform. Results show drastic performance improvements by applying OWL2 RL rule subsets, allowing for performant reasoning for small datasets on mobile systems.
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