118,409 research outputs found
A Reasoner for Calendric and Temporal Data
Calendric and temporal data are omnipresent in countless
Web and Semantic Web applications and Web services. Calendric and
temporal data are probably more than any other data a subject to
interpretation, in almost any case depending on some cultural, legal,
professional, and/or locational context. On the current Web, calendric
and temporal data can hardly be interpreted by computers. This article
contributes to the Semantic Web, an endeavor aiming at enhancing
the current Web with well-defined meaning and to enable computers to
meaningfully process data. The contribution is a reasoner for calendric
and temporal data. This reasoner is part of CaTTS, a type language for
calendar definitions. The reasoner is based on a \theory reasoning" approach
using constraint solving techniques. This reasoner complements
general purpose \axiomatic reasoning" approaches for the Semantic Web
as widely used with ontology languages like OWL or RDF
A Reasoner for Calendric and Temporal Data
Calendric and temporal data are omnipresent in countless
Web and Semantic Web applications and Web services. Calendric and
temporal data are probably more than any other data a subject to
interpretation, in almost any case depending on some cultural, legal,
professional, and/or locational context. On the current Web, calendric
and temporal data can hardly be interpreted by computers. This article
contributes to the Semantic Web, an endeavor aiming at enhancing
the current Web with well-defined meaning and to enable computers to
meaningfully process data. The contribution is a reasoner for calendric
and temporal data. This reasoner is part of CaTTS, a type language for
calendar definitions. The reasoner is based on a "theory reasoning" approach
using constraint solving techniques. This reasoner complements
general purpose "axiomatic reasoning" approaches for the Semantic Web
as widely used with ontology languages like OWL or RDF
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
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