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Web GIS & Semantic Web Services Vlad KMi for the the 2nd AKT DTA Symposium Aberdeen, UK, January 23, 2006 Supervision team: John Domingue,

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Presentation on theme: "Web GIS & Semantic Web Services Vlad KMi for the the 2nd AKT DTA Symposium Aberdeen, UK, January 23, 2006 Supervision team: John Domingue,"— Presentation transcript:

1 Web GIS & Semantic Web Services Vlad Tanasescu @ KMi for the the 2nd AKT DTA Symposium Aberdeen, UK, January 23, 2006 Supervision team: John Domingue, Enrico Motta Thanks to: DIP project, Jon Bryant (BT Research), Alessio Gugliotta (KMi)

2 GIS?

3 Geographical Information System

4 dedicated software for digitized geographical data Philippe Rigaux, Michel Scholl, and Agnes Voisard, Spatial Databases with Application to GIS, Morgan Kaufmann, 2002

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10 A GIS is MORE than a mapping tool

11 SELECT C1.Name, R1.Name FROM City C1, River R1 WHERE Distance(C1.Shape,R1.Shape) <= ALL (SELECT Distance(C2.Shape) FROM City C2 WHERE C1.Name <> C2.Name)

12 Semantic Web Services?

13 The Semantic Web

14 Web (URI, HTML, HTTP) Web Services (UDDI, WSDL, SOAP, REST) Semantic Web (RDF, OWL) Semantic Web Services Dynamic Static SyntaxSemantics

15 Objectives that a client wants to achieve by using Web Services Semantic description of Web Services: - Capability (functional) - Interfaces (usage) Provide the formally specified terminology of the information used by all other components Connectors between components with mediation facilities for handling heterogeneities WSMO MEDIATORS GOALS WEB SERVICESONTOLOGIES

16 Client Services IRS-III

17 Web GIS?

18 NO… proprietary data multiple formats common user needs

19 But lot of Web Maps!

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22 Web Maps APIs: a revolution?

23 Quite!

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26 Popularity?

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28 Limits? data heterogeneity lack of semantics expressiveness

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30 So? spatial ontologies semantic web services build a semantic web GIS

31 Spatial ontologies are… special

32 Objects vs Places vs Fields Boundaries as entities? Level of detail Naïve Geography etc

33 SGIS: a multi layer architecture

34 DIP WP9 Scenario: Snowstorm Emergency Planning

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36 Capture user practices Goal oriented Weather spatial specifics Merge heterogeneous data etc

37 Weather Hazards Details Identify affected roads information Identify affected airport Information Identify suitable shelter and rest centres Identify locations of rescue options GOALS

38 WEB SERVICES

39 WEB FRONT-END

40 Thanks Again, also to: the DIP WP9 team, Jon Bryant (BT Research) and Alessio Gugliotta (KMi)


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