Abstract
Semantic technologies have proved to be a suitable foundation for integrating Big Data applications. Wireless Sensor Networks (WSNs) represent a common domain which knowledge bases are naturally modeled through ontologies. In our previous works we have built domain ontology of WSN for water quality monitoring. The SSN ontology was extended to meet the requirements for classifying water bodies into appropriate statuses based on different regulation authorities. In this paper we extend this ontology with a module for identifying the possible sources of pollution. To infer new implicit knowledge from the knowledge bases different rule systems have been layered over ontologies by state-of-the-art WSN systems. A production rules system was developed to demonstrate how our ontology can be used to enable water quality monitoring. The paper presents an example of system validation with simulated data, but it is developed for use within the InWaterSense project with real data. It demonstrates how Biochemical Oxygen Demand observations are classified based on Water Framework Directive regulation standard and provide its eventual sources of pollution. The system features and challenges are discussed by also suggesting the potential directions of Semantic Web rule layer developments for reasoning with stream data.
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Jajaga, E., Ahmedi, L., Ahmedi, F. (2015). An Expert System for Water Quality Monitoring Based on Ontology. In: Garoufallou, E., Hartley, R., Gaitanou, P. (eds) Metadata and Semantics Research. MTSR 2015. Communications in Computer and Information Science, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-319-24129-6_8
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