An Expert System for Water Quality Monitoring Based on Ontology

Jajaga, Edmond; Ahmedi, Lule; Ahmedi, Figene

doi:10.1007/978-3-319-24129-6_8

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…However, a deeper research in this direction remains per future work. We also plan to compare StreamJess against the envisioned pure Semantic Web system (Jajaga and Ahmedi, 2015). Furthermore, our future work also includes enabling temporal operators (serial, sequence, etc.)…”

Section: Resultsmentioning

confidence: 99%

“…Both forwardchaining and backward-chaining are used collectively during the inference process Chau (2007). Even though that these approaches, together with our previous work (Jajaga et al, 2015) argue that pairing ontologies with production rules provides a fruitful solution, they do not make any distinction between stream and static data. As such, they do not implement the window feature.…”

Section: Hybrid Approachesmentioning

confidence: 99%

“…Rules provide an effective mechanism for applying changes on the knowledge base induced by streams. Although layering different rule systems over ontologies has already been suggested (Jajaga and Ahmedi, 2015), using Semantic Web standard rule languages, SWRL (Horrocks et al, 2015) and RIF (Boley et al, 2007), over stream data has to the best of our knowledge not been considered to date. The ideal SR system would be to implement rule-based reasoning tasks within the Semantic Web platform.…”

Section: Introductionmentioning

confidence: 99%

“…The ideal SR system would be to implement rule-based reasoning tasks within the Semantic Web platform. In line with this vision, we have previously developed the INWATERSENSE (INWS) ontology (Ahmedi et al, 2013) and an expert system (Jajaga et al, 2015) demonstrating its usage. In this paper we apply Java Expert System Shell (Jess) (Hill, 2003) to reason over stream data sets.…”

Section: Introductionmentioning

confidence: 99%

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StreamJess: a stream reasoning framework for water quality monitoring

Jajaga

Ahmedi

2016

IJMSO

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Stream data knowledge bases modeled 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 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. The system also identifies the potential sources of pollution by also extending our ontology with the pollutants module. The solution utilizes C-SPARQL abilities to filter and aggregate RDF streams on windows to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behavior.

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Section: Resultsmentioning

confidence: 99%

Section: Hybrid Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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StreamJess: a stream reasoning framework for water quality monitoring

Jajaga

Ahmedi

2016

IJMSO

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“…In contrast, the highest level of metadata structure incorporates formally-specified ontologies that encapsulate domain-specific knowledge in a hierarchy of and relations among the terms of a vocabulary. Data annotated in this way can be used to derive new knowledge through automated reasoning as illustrated for water quality monitoring [Jajaga et al, 2015].…”

Section: Approximate Location For Figure 1]mentioning

confidence: 99%

Perspectives from a water research institute on knowledge management for sustainable water management

Hering¹,

Nunnenmacher²,

Waldow³

2017

Preprint

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A sizeable, and sometimes overwhelming, array of knowledge is available and relevant to Sustainable Water Management (SWM). This knowledge derives not only from the natural, social and engineering sciences but also from practical experiences in water management, which is defined broadly to include all forms of water use as well as the protection of water resources and reduction of risk from water-related hazards. As a publically-funded water research institute, Eawag (the Swiss Federal Institute of Aquatic Science and Technology) faces the challenges of integrating and synthesizing available knowledge, creating new knowledge through research projects and making knowledge available in an accessible form to support engineering practice, water resources management and related policy-making. This chapter examines these challenges from an organizational perspective, discussing measures taken within Eawag to meet them and identifying remaining gaps and possible ways to address them.

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