Resource-Constrained Reasoning Using a Reasoner Composition Approach Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country

Abstract: The marriage between semantic web and sensor-rich systems can semantically link the data related to the physical world with the existent machined-readable domain knowledge encoded on the web. This has allowed a better understanding of the inherently heterogeneous sensor data by allowing data access and processing based on semantics. Research in different domains has been started seeking a better utilization of data in this manner. Such research often assumes that the semantic data is processed in a centralized server and that reliable network connections are always available, which are not realistic in some critical situations. For such critical situations, a more robust semantic system needs to have some local-autonomy and hence on-device semantic data processing is required. As a key enabling part for this strategy, semantic reasoning needs to be developed for resource-constrained environments. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only the amount of reasoning needed for the ontology to be reasoned over) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms have been designed and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, facilitating greater semantic data processing on sensor devices