Describing and communicating uncertainty within the semantic web

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Abstract

The Semantic Web relies on carefully structured, well defined, data to allow machines to communicate and understand one another. In many domains (e.g. geospatial) the data being described contains some uncertainty, often due to incomplete knowledge; meaningful processing of this data requires these uncertainties to be carefully analysed and integrated into the process chain. Currently, within the SemanticWeb there is no standard mechanism for interoperable description and exchange of uncertain information, which renders the automated processing of such information implausible, particularly where error must be considered and captured as it propagates through a processing sequence. In particular we adopt a Bayesian perspective and focus on the case where the inputs / outputs are naturally treated as random variables. This paper discusses a solution to the problem in the form of the Uncertainty Markup Language (UncertML). UncertML is a conceptual model, realised as an XML schema, that allows uncertainty to be quantified in a variety of ways i.e. realisations, statistics and probability distributions. UncertML is based upon a soft-typed XML schema design that provides a generic framework from which any statistic or distribution may be created. Making extensive use of Geography Markup Language (GML) dictionaries, UncertML provides a collection of definitions for common uncertainty types. Containing both written descriptions and mathematical functions, encoded as MathML, the definitions within these dictionaries provide a robust mechanism for defining any statistic or distribution and can be easily extended. Universal Resource Identifiers (URIs) are used to introduce semantics to the soft-typed elements by linking to these dictionary definitions. The INTAMAP (INTeroperability and Automated MAPping) project provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. The interpolation service uses the information within these observations to influence the prediction outcome. The output uncertainties may be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

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  • Williams2008ISWC.pdf

    Rights statement: Williams, Matthew; Cornford, Dan; Bastin, Lucy; Ingram, Ben : Describing and communicating uncertainty within the semantic web. Proc. of the Fourth International Workshop on Uncertainty Reasoning for the Semantic Web, 2008, ceur-ws.org/Vol-423/paper3.pdf

    Final published version, 144 KB, PDF-document

Details

Publication dateDec 2008
Publication titleProceedings of the Fourth International Workshop on Uncertainty Reasoning for the Semantic Web
EditorsFernando Bobillo, Paulo C.G. da Costa, et al
PublisherCEUR-WS.org
Original languageEnglish
EventUncertainty Reasoning for the Semantic Web Workshop, part of 7th International Semantic Web Conference - Karlsruhe (DE), Denmark

Publication series

NameCEUR Workshop Proceedings
Publisher1613-0073
Volume423

Workshop

WorkshopUncertainty Reasoning for the Semantic Web Workshop, part of 7th International Semantic Web Conference
Abbreviated titleURSW '08
CountryDenmark
CityKarlsruhe (DE)
Period26/10/08 → …

Bibliographic note

Williams, Matthew; Cornford, Dan; Bastin, Lucy; Ingram, Ben : Describing and communicating uncertainty within the semantic web. Proc. of the Fourth International Workshop on Uncertainty Reasoning for the Semantic Web, 2008, ceur-ws.org/Vol-423/paper3.pdf

    Keywords

  • semantic web, uncertainty, SemanticWeb, interoperable description, exchange of uncertain information, error, Bayesian perspective, random variables, Uncertainty Markup Language, UncertML, soft-typed XML schema design, Geography Markup Language, MathML, semantics to the soft-typed elements by linking to these dictionary definitions., The INTAMAP (INTeroperability and Automated MAPping) project provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. The interpolation service uses the information within these observations to influence the prediction outcome. The output uncertainties may be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

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