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Ontology-based Discovery and Composition of Geographic Information Services

Ontology-based Discovery and Composition of Geographic Information Services. Michael Lutz TU Wien, Research Group Geoinformation April 26 th , 2006. Overview. JRC – Spatial Data Infrastructures Unit Ontology-based service discovery Data access services Geoprocessing services

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Ontology-based Discovery and Composition of Geographic Information Services

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

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

  3. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  4. 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. Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

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

  8. Service Composition • Creatingvalue-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 Michael Lutz – Ontology-based GI Service Discovery & Composition

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

  10. Service Discovery & Composition Michael Lutz – Ontology-based GI Service Discovery & Composition

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

  12. Problem – Accessing Data Today • Syntactic descriptionsof the schema often not sufficientfor interpreting the attributes • difficult to create meaningful query expressions or extract data for further processing Michael Lutz – Ontology-based GI Service Discovery & Composition

  13. Ontologies for Discovering GI Services • An ontologyis 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) Michael Lutz – Ontology-based GI Service Discovery & Composition

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

  15. Discovering Data Access Services • User Interface built dynamically from selected ontologies • AutomaticallyderivesDL query concept • Queries SemanticCatalogue Service • Can also be used for retrieving discovered data Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

  18. 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 • Matchingalso pre- & postconditions • requires FOL theorem provers  expensive Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

  21. 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 • Match inputs & outputs • DL subsumption reasoning • efficiently filter out potential matches • Match pre- & postconditions • FOL theorem prover • select most appropriate service(s) Michael Lutz – Ontology-based GI Service Discovery & Composition

  22. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  23. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  24. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  25. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  26. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

  27. 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. Michael Lutz – Ontology-based GI Service Discovery & Composition

  28. ArchitectureThe Basic Service Chain Michael Lutz – Ontology-based GI Service Discovery & Composition

  29. ArchitectureIntegrating user-defined data sets Michael Lutz – Ontology-based GI Service Discovery & Composition

  30. ArchitectureSemantic Service Orchestration Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

  33. Building Domain Ontologies • Define ranges(and domains) of roles • Define conceptsusing 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 GMLgeometry types(e.g. point or polygon) • value comparisons can be used in query statements Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

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

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

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

  39. 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 conceptfor feature type: (define-concept query (and Measurement (some quantityResult (all observable WaterLevel) (all unitOfMeasure Centimeter))))  Result: e.g. chmi_Measurement Michael Lutz – Ontology-based GI Service Discovery & Composition

  40. 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 Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

  43. Matchmaking – Function Subtypes • Matchmaking based on function subtypes • safe substitution • if f1 is a subtype of f2, it can be used instead of f2 f1 is a match for query f2 Michael Lutz – Ontology-based GI Service Discovery & Composition

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

  45. Ontology-based Descriptions & Metadata Michael Lutz – Ontology-based GI Service Discovery & Composition

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

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

  48. Distances – Conceptualisation • Based on R3 (incl. metric) as a reference space • Primitives include • curve.Curve between two points in R3 (ternary predicate) • length. Function returning the length of a curve • plane, sphere, network etc.Unary predicates that represent particular subspaces of R3 • shortestCurve.Shortest curve between two points in a particular subspace of R3 (quaternary predicate) Michael Lutz – Ontology-based GI Service Discovery & Composition

  49. Domain-level Operation Description • distanceoperation between the points p1 and p2 • pre:p1 and p2 are in the same subspace of R3 • post: length of the shortest (existing) curve in a particular subspace of R3between p1 and p2 Michael Lutz – Ontology-based GI Service Discovery & Composition

  50. Rule-based Approach to GI Discovery • “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 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) Michael Lutz – Ontology-based GI Service Discovery & Composition

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