1. UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY WITHIN THE (SEMANTIC) WEB Matthew Williams williamw@aston.ac.uk

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

3. MOTIVATION The semantic and sensor webs

4. THE SEMANTIC WEB Most Web content today is designed for humans to read, not computers. Semantic Web will bring structure to the meaningful content of Web pages. Adding logic to the Web allows rules to be used for inference. Ontologies are used to describe entities and relations between entities.

5. HOW UNCERTAINTY IS USED WITHIN THE SEMANTIC WEB PW-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 – NOT freely available. Other standards looking at similar concepts: BayesOWL. FuzzyOWL.

6. THE SENSOR WEB

7. 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

8. WHAT IS MISSING? A formal open standard for quantifying complex uncertainties: Distributions. Statistics. Realisations.

9. UNCERTML I’ve done it!!

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

11. STATISTICS 12.08

12. DISTRIBUTIONS 34.564 67.45

13. REALISATIONS 100

14. UNCERTML Difficult decisions and design principles

15. 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-typedStrong-typed... Bitumen... Bitumen

16. 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).

17. UNCERTML – DICTIONARY EXAMPLE All Probability Distributions This is a dictionary... This is a Gaussian distribution Gaussian Normal This is a cumulative distribution function Cumulative Distribution Function 1 2

18. 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!

19. UNCERTML WITHIN THE SEMANTIC WEB Proprietary software can impede interoperability which is detrimental to the Semantic Web. Discrete probability tables can only provide so much information. Provide an open standard for describing the complex probability distributions that are currently lacking within PR-OWL.

20. UNCERTML WITHIN THE SENSOR WEB resultQuality of an O&M Observation. Encode sensor bias and other inherent uncertainties of a sensor observation. Quality property of SWE types. Effectively provides a ‘Random Variable’ type. Positional uncertainty within GML. Extending GML would allow UncertML to integrate with the geometry types to provide positional uncertainty information.

21. UNCERTML Does it actually work??

22. 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.

23. 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 ‘Really clever’ Bayesian inference: Different sensor errors. Change of support. Fast & approximate algorithms.

24. COMPARING PREDICTIONS WITH AND WITHOUT UNCERTML Without UncertMLWith UncertML

25. CONCLUSIONS Currently no existing standard to describe uncertainty within the Semantic and Sensor Webs. UncertML provides an extensible, weak-typed, design that can quantify uncertainty using: Distributions. Statistics. Realisations. Provide more information for use in decision support systems – especially useful in risk management.