StreamJess: a stream reasoning framework for water quality monitoring. (2016)

• Record Type: Journal Article
• Authors: Jajaga, Edmond; Ahmedi, Lule; Ahmedi, Figene
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 11:Number 4(2016)
• Page Start: 207
• Page End: 220
• Publication Date: 2016
• Subjects: stream data -- expert system -- reasoning -- Jess -- rules -- ontologies -- water quality monitoring -- SWRL -- surface water classification -- SPARQL -- sources of water pollution -- Rete; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

Abstract

Stream data knowledge bases modelled with OWL are a proved natural approach. But, querying and reasoning over these knowledge bases is not supported with standard Semantic Web technologies like SPARQL and SWRL. Query processing systems enable querying, but to the best of our knowledge, Semantic Web rules are still unable to handle the required reasoning features for effective inference over stream data, i.e. non-monotonic, closed-world and time-aware reasoning. In absence of such a system, we showed in our previous work how Jess can be used for monitoring water quality, but by bringing input data manually. In this paper, we enable stream data support and thus a timely detection of faulty water quality statuses by also extending our ontology with the pollutants module. C-SPARQL abilities to filter and aggregate RDF streams are utilised to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behaviour.