1. Michael Lutz – Ontology-based GI Service Discovery & Composition TU Wien, 26.04.2006 Ontology-based Discovery and Composition of Geographic Information Services Michael Lutz TU Wien, Research Group Geoinformation April 26 th, 2006

2. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Overview JRC – Spatial Data Infrastructures Unit Ontology-based service discovery  Data access services  Geoprocessing services  Integration in SDI An SDI experiment for disaster management

3. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Joint Research Centre Mission: provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies Service of the European Commission (EC)  Coordinates numerous EU-wide networks  Carries out studies and experiments in our own laboratories on behalf of customer institutions  Participates in projects  Liaises with a variety of non-EU and global scientific and standard-setting bodies

4. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Spatial Data Infrastructures (SDI) Unit Mission: coordinate the scientific and technical development and implementation of INSPIRE INSPIRE: Infrastructure for Spatial Information in Europe  provide integrated GI services that should allow users to identify and access GI (from local to global level), in an interoperable way for a variety of uses.  target users include policy-makers at European, national and local level and the citizen.

5. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Where is the closest place to eat which is still open? Current Location “Restaurants”“Hotels” GI Service Discovery in SDIs – Use Case

6. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Where is the closest place to eat which is still open? 0,9 km 1,0 km 0,5 km 1,9 km Current Location GI Service Discovery in SDIs – Use Case

7. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Spatial Data Infrastructures Goal: efficient provision & access to  distributed, heterogeneous geographic information  in a loosely coupled manner Standardised service interfaces for  discovering data & services – Catalogue Services  accessing data – WFS, WCS (data access services)  viewing data – WMS  processing – WPS (geoprocessing services)

8. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Service Composition Creating value-added (complex) service chains from simple component services  e.g. data access + geoprocessing services Service discovery is an important part of service composition  goal: find appropriate and matching services

9. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition inputs & outputs functionality meaning of feature type Service Discovery & Composition

10. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Service Discovery & Composition

11. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Problem – Searching in SDIs Today Mainly based on matching keywords and other search terms with metadata entries  different terminology  low recall  low expressivity  low precision Difficult to express functionality

12. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Problem – Accessing Data Today Syntactic descriptions of the schema often not sufficient for interpreting the attributes difficult to create meaningful query expressions or extract data for further processing

13. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Ontologies for Discovering GI Services An ontology is an explicit formal specification of a shared conceptualization Ontologies can enrich GI metadata  semantics become machine-interpretable  concise and expressive queries Logical reasoning on ontology concepts  implicit relationships  flexible classification of information Languages:  Description Logics (DL)  First-Order Logic (FOL)

14. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition DL subsumption reasoning Where is the closest place to eat which is still open? based on Domain Ontology DL description of the query concept “place to eat” (with location & opening hours) Query concept DL description of the application concept “Restaurant” Application Ontology Concept Discovering Data Access Services

15. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Discovering Data Access Services User Interface built dynamically from selected ontologies Automatically derives DL query concept Queries Semantic Catalogue Service Can also be used for retrieving discovered data

16. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition 2. define query using shared vocabulary (resembling SQL select statement) Architecture 1. request for shared vocabulary 6. catalogue request 3. derive DL query concepts for feature type 5. build catalogue query 4. request for matching concepts

17. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition 2. define query using shared vocabulary (resembling SQL select statement) Architecture 1. request for shared vocabulary 3. derive DL query concepts for feature type 5. build catalogue query 4. request for matching concepts 6. catalogue request 9. GetFeature 8. derive WFS query

18. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Discovering Geoprocessing Services Shared vocabularies (domain ontologies)  do not contain information on operations Matching only inputs & outputs  often without shared vocabularies  low recall  not expressive enough  low precision Matching also pre- & postconditions  requires FOL theorem provers  expensive

19. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Operation description of the provided operation two-step matchmaking Where is the closest place to eat which is still open? based on Domain-level Operation Description Operation description of the required operation Semantic Query Semantic Advertisement Discovering Geoprocessing Services

20. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition For each service advertisement and request, define  a semantic signature (inputs & outputs) with references to DL concepts  pre- & postconditions in FOL Operation Descriptions

21. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Matchmaking Based on function subtypes  if a is a subtype of q, a can be used instead of q  a is a match for q 1.Match inputs & outputs  DL subsumption reasoning  efficiently filter out potential matches 2.Match pre- & postconditions  FOL theorem prover  select most appropriate service(s)

22. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Integration within SDI Components:  Semantic Catalogue Service  Semantic Catalogue Client  Ontology Management Service  DL Reasoner and FOL Theorem Prover  Integrate ontology-based descriptions into existing metadata

23. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Conclusion GI service composition requires expressive and strict discovery Keyword-based methods have low recall & precision Matchmaking with ontology-based service descriptions can enhance catalogue search Successful integration in SDI workflows

24. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Open Issues Ontologies do not solve the “metadata trap” Usability of ontology-based user interfaces  especially for FOL “Soft” matchmaking methods (similarity)  different use cases Granularity of GI service discovery  task ontologies

25. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition A Pilot for Disaster Management Test the ORCHESTRA architecture for pan-European hazard assessing Focus on risks related to natural hazards (flooding, droughts, forest fires). Support decision makers in the EC to more efficiently integrate European information:  to assess the risk of forest fires in the EU Member States and to support forest fire prevention.  to assess the vulnerability to various hazards (floods, droughts, etc.) in the EU Member States

26. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition A Pilot for Disaster Management Pilot should enable stakeholders to access assessments in an interoperable and also interactive manner (more than static maps) Experts, Stakeholders and Users  Experts that conduct policy support towards various EC DG’s in the context of forest fires, droughts and flooding  Decision makers within the DGs ENV & REGIO  later possibly also national decision makers/stakeholders

27. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Addressed Technical Aspects Schema mapping from heterogeneous national data sources (spatial & non spatial data) into a common pan-European model Distributed geo-processing to support ad- hoc analysis focussing on combination of GI and spatial decision support Support interactive web-based assessment of hazards/vulnerabilities Use ontologies for derive schema mappings and to describe hazard/vulnerability analysis tasks.

28. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Architecture The Basic Service Chain

29. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Architecture Integrating user-defined data sets

30. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Architecture Semantic Service Orchestration

31. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Thanks for your attention! http://ifgi.uni-muenster.de/~lutzm

32. Michael Lutz – Ontology-based GI Service Discovery & Composition TU Wien, 26.04.2006 Ontology-based Discovery and Composition of Geographic Information Services Additional Slides TU Wien, Research Group Geoinformation April 26 th, 2006

33. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Building Domain Ontologies Define ranges (and domains) of roles Define concepts using existing roles  cardinality constraints and value restrictions for further constraining the range of a role Map ranges of roles to XML schema datatypes (e.g. string or decimal) or simple GML geometry types (e.g. point or polygon)  value comparisons can be used in query statements

34. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Domain Ontologies – Example

35. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Domain Ontologies – Example

36. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Building Application Ontologies Same guidelines as for domain ontologies One concept representing a feature type  derive from existing concept in domain ontology  (all-quantified) value restrictions  cardinality constraints  additional roles Query Concepts defined using domain roles

37. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Application Ontologies – Examples

38. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Application Ontologies & Query Concepts – Subsumption Hierarchy

39. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition User Query → DL Query Concept User query: SELECT x.quantityResult.value, x.timeStamp FROM Measurement x WHERE (x.quantityResult.observable hasType WaterLevel) AND (x.quantityResult.unit hasType Centimeter) AND (x.quantityResult.value >= 300) AND (x.timeStamp isBefore 12:00:00) AND (x.location isWithinBoundingBox (12,23,45,25)) DL query concept for feature type: (define-concept query (and Measurement (some quantityResult (all observable WaterLevel) (all unitOfMeasure Centimeter))))  Result: e.g. chmi_Measurement

40. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Registration Mapping for GI Retrieval Mapping between XML and ontology structures For deriving WFS query and filter expression /StavVody  chmi_Measurement /StavVody/gml:position/gml:Point  chmi_Measurement.location /StavVody/tok/text()  chmi_Measurement.chmi_qRWaterLevel.observedWaterBody.name /StavVody/stanice/text()  chmi_Measurement.name /StavVody/stav  chmi_Measurement.chmi_qRWaterLevel /StavVody/stav/text()  chmi_Measurement.chmi_qRWaterLevel.value /StavVody/prutok  chmi_Measurement.chmi_qRDischarge /StavVody/prutok/text()  chmi_Measurement.chmi_qRDischarge.value /StavVody/datum/text()  chmi_Measurement.timeStamp /StavVody  chmi_Measurement /StavVody/gml:position/gml:Point  chmi_Measurement.location /StavVody/tok/text()  chmi_Measurement.chmi_qRWaterLevel.observedWaterBody.name /StavVody/stanice/text()  chmi_Measurement.name /StavVody/stav  chmi_Measurement.chmi_qRWaterLevel /StavVody/stav/text()  chmi_Measurement.chmi_qRWaterLevel.value /StavVody/prutok  chmi_Measurement.chmi_qRDischarge /StavVody/prutok/text()  chmi_Measurement.chmi_qRDischarge.value /StavVody/datum/text()  chmi_Measurement.timeStamp

41. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Semantic Advertisements and Queries

42. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Methodology – Matchmaking Procedure

43. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Matchmaking – Function Subtypes Matchmaking based on function subtypes safe substitution  if f 1 is a subtype of f 2, it can be used instead of f 2  f 1 is a match for query f 2

44. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Matchmaking Matchmaking based on function subtypes 1.Matching Inputs & Outputs using DL subsumption reasoning 2.Matching pre- & postconditions using a FOL theorem prover

45. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Ontology-based Descriptions & Metadata

46. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Workflow for Registering Services 2. get domain operations 1. select domains 4. select operation 6. add constraints 7. add constraints on other metadata fields 3. get domain vocabularies 5. get operation specification 8. register service metadata 9. store semantic advertisement 10. store metadata (incl. reference to semantic advertisement 11. store application ontology

47. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition 10. get superconcepts of requested inputs and subconcepts of requested outputs Workflow for Service Discovery 1-7. same as service registration 8. send request (incl. semantic query) 9. get DL application ontologies 12. get FOL domain ontologies 14. test proof obligations 11. retrieve matching advertisements 13. for each matching advertisement: generate proof obligations for predicate and plug-in post match

48. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Distances – Conceptualisation Based on R 3 (incl. metric) as a reference space Primitives include  curve. Curve between two points in R 3 (ternary predicate)  length. Function returning the length of a curve  plane, sphere, network etc. Unary predicates that represent particular subspaces of R 3  shortestCurve. Shortest curve between two points in a particular subspace of R 3 (quaternary predicate)

49. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Domain-level Operation Description distance operation between the points p 1 and p 2 pre: p 1 and p 2 are in the same subspace of R 3 post: length of the shortest (existing) curve in a particular subspace of R 3 between p 1 and p 2

50. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Rule-based Approach to GI Discovery DAFIF_Airport(a), icao_code(a,i), … => Airport(a), icao(a,i),... Airport(a), Runway(r), hasPart(a,r), length(r,l), l>5000 => C5CapableAirport(a) Airport(ap), icao(a,icao), Runway(rw), icao(rw,icao) => hasPart(ap,rw) “reproduce” GML schema in OWL mapping rules (horn clauses) from OWL “application schema” to domain ontology  possible to create OWL instances from data and  run inferences (forward/backward chaining) on them requires sophisticated discovery procedure

51. TU Wien, 26.04.2006Michael Lutz – Ontology-based GI Service Discovery & Composition Rule-based Approach to Discovering Data Access Services