1. UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY Matthew Williams williamw@aston.ac.uk

2. OVERVIEW Introduction. Motivation – the Semantic and Sensor Webs. UncertML overview. Use case – The INTAMAP project. Conclusions.

3. MOTIVATION The semantic and sensor webs

4. THE SENSOR WEB

5. SENSOR WEB ENABLEMENT (SWE) Open Geospatial Consortium (OGC) initiative Interoperability interfaces and metadata encodings. Real time integration of heterogeneous sensor webs into the information infrastructure. Current SWE standards Observations & Measurements SensorML SWE Common No formal standard for quantifying uncertainty -0.02 0.02 25.3

6. HOW UNCERTAINTY IS USED WITHIN THE SEMANTIC WEB PR-OWL: a Bayesian Ontology Language for the Semantic Web: Extends OWL to allow probabilistic knowledge to be represented in an ontology. Used for reasoning with Bayesian inference. Random variables are described by either a PR-OWL table (discrete probability) or using a proprietary format. Other standards looking at similar concepts: BayesOWL. FuzzyOWL.

7. What next? A formal open standard for quantifying complex uncertainties Extend to allow continuous distributions More powerful reasoning, richer representations

8. UNCERTML

9. OVERVIEW Split into three distinct packages (distributions, statistics & realisations).

10. DISTRIBUTIONS 34.564 67.45

11. UNCERTML An overview

12. WEAK VS. STRONG Benefits Generic features have generic properties – extensible Drawbacks Validation becomes less meaningful Benefits Produces relatively simple XML features Drawbacks Not easily extended – all domain features must be known a priori Weak-typed Strong-typed 34.2 12.4 34.2 12.4

13. THE UNCERTML DICTIONARY Weak-typed designs rely on dictionaries. Includes definitions of key distributions & statistics. URIs link to dictionary entry and provide semantics. Could be written in Semantic Web standards (OWL, RDF etc).

14. All Probability Distributions Distributions dictionary Gaussian distribution Gaussian Normal cumulative distribution function Cumulative Distribution Function 1 2 UNCERTML – DICTIONARY EXAMPLE

15. SEPARATION OF CONCERNS Several competing standards already exist addressing the issue of units and location. Geospatial information not always relevant – Systems biology. Do what we know – do it well!

16. UNCERTML An applied case study

17. THE INTAMAP PROJECT An automatic, interoperable service providing real time interpolation between observations. EURDEP providing radiological data as a case study. Provide real time predictions to aid risk management through a Web Processing Service interface.

18. UNCERTML IN INTAMAP ‘Really clever’ Bayesian inference: Different sensor errors. Change of support. Fast & approximate algorithms.

19. COMPARING PREDICTIONS WITH AND WITHOUT UNCERTML Without UncertMLWith UncertML

20. CONCLUSIONS Currently no interoperable standard which fully describes random variables. UncertML provides an extensible, weak-typed, design that can quantify uncertainty using: Distributions. Statistics. Realisations. Provide richer information for use in decision support systems.

21. UNCERTML IN INTAMAP 0.0 3.6 52.4773635864 -1.89538836479 19.4 5 35.2,56.75 31.2,65.31 28.2,54.23 35.6,45.21 41.5,85.24