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Trustworthy Semantic Webs Building Geospatial Semantic Webs

Trustworthy Semantic Webs Building Geospatial Semantic Webs. Dr. Bhavani Thuraisingham The University of Texas at Dallas October 2006 Presented at OGC Meeting, October 4, 2006. Outline. Semantic Web Definition, Components, Applications Geospatial Semantic Web

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Trustworthy Semantic Webs Building Geospatial Semantic Webs

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  1. Trustworthy Semantic WebsBuilding Geospatial Semantic Webs Dr. Bhavani Thuraisingham The University of Texas at Dallas October 2006 Presented at OGC Meeting, October 4, 2006

  2. Outline • Semantic Web • Definition, Components, Applications • Geospatial Semantic Web • Definition, Components, Applications • Collaboration with Prof. Latifur Khan and Students: Alam Ashraful and Ganesh Subbiah at UT Dallas • Security • Collaboration with Prof. Michael Gertz at UC Davis and Prof. Elisa Bertino at Purdue U. • Directions

  3. What is the Semantic Web? • Machine understandable web pages; Activities on the web such as searching with little or no human intervention • Vision of Tim Berners Lee, www.w3c.org • Applications include interoperability, web services, e-business

  4. Need for Geospatial Semantic Web * Semantic Metadata Extraction * Decision Centric Fusion * Geospatial data interoperability through web services * Geospatial data mining * Data Source A Tools for Analysts Data Source B SECURITY/ QUALITY Data Source C

  5. TRUST P R I V A C Y Logic, Proof and Trust Rules/Query Other Services GRDF, Geospatial Ontologies GML, GML Schemas Protocols Vision for Geospatial Semantic Web • Adapted from Tim Berners Lee’s description of the Semantic Web

  6. GML • Standardized geospatial schemas from OGC • A set of XML schemas to encode geospatial data • Most recent version (3.1.1) also allows images to attach geographic data using GML • Application developers from disparate geospatial domains extend the core schemas for their applications. • Reduces non-interoperability problems. GML Coverage Schema Topology Schema Geometry Schemas Coordinate Schema

  7. GRDF • GRDF (Geospatial Resource Description Framework) • Adds semantics to data • Loosely-structured (easy to freely mix with other non-geospatial data) • Semantically extensible ComputerScience Building (33.98111, -96.4011) (33.989999, -96.4022) hasExtent

  8. GRDF Example (Topology Ontology) <owl:Class rdf:ID=“Edge"></owl:Class> <owl:Class rdf:ID=“Node"></owl:Class> <owl:Class rdf:ID=“Face"> <rdfs:subClassOf> <owl:Restriction> <owl:minCardinality rdf:datatype="http://www.w3.org/2001/XMLSchema#int" >1</owl:minCardinality> <owl:onProperty> <owl:DataTypeProperty rdf:ID=“hasEdge"/> </owl:onProperty> </owl:Restriction> … </owl:Class>

  9. Geospatial Ontology Upper-level ontologies Abstract Definitions of Main Geospatial Concepts Mid-level ontology (GRDF) Concrete Definitions of All Relevant Geospatial Concepts Domain ontologies Hydrology ontology Cartography ontology Image ontology

  10. Geospatial Ontology • OWL-S for describing Geospatial Semantic Web Services • Developing Geospatial domain specific Ontology using OWL-DL for Geospatial Semantic Web services • Modular, Bottom-up Approach • Ontology shared between the Service Provider and Service Requestor • Input and Output parameters of the geospatial web services (WSDL) mapped to the concepts in the OWL-DL Ontology OWL-DL Geospatial Domain Ontology (Snapshot) OWL-S Geospatial Semantic Web Service

  11. Applications: Geospatial Web Services • DAGIS (Discovery of Annotated Geospatial Information Services) Semantic Web Services framework to provide an integrated solution for realizing the vision of the Geospatial Semantic Web. • A single interface to search, retrieve and update the Geospatial data with secured end-to-end semantics. Query Results for ‘within’ Geospatial operator Map Result for ‘Between’ Geospatial Operator

  12. Applications: Geospatial Data Interoperability • Current state-of-the-art is static or semi-automatic • On-the-fly “Knowledge Discovery” requires automated data integration techniques • Proposed solution:GML Schemas and OGC Standards solves Syntactic Heterogeneities • Semantics Issue: Existing frameworks lack data semantics. Solution needed! • Geospatial Data Semantics enables Knowledge Discovery • Discovery of explicit knowledge is good • Discovery of implicit/tacit knowledge is great • Logic based inferential frameworks DAGIS Integration Scenarios

  13. Testing Image Pixels Training Image Pixels SVM Classifier Classified Pixels Region Growing Graph of Regions Shortest Path Tree Graph of Near Neighboring Regions Ontology Driven Rule Mining High Level Concept Data Mining: Ontology-Driven Classification

  14. Classification: Support Vector Machine (SVM) Accuracy of Various Classifiers

  15. Framework for Geospatial Data Security Collaboration with UC Davis (Prof. Michael Gertz) and Purdue U. (Prof. Elisa Bertino)

  16. Security: Semantic Access Control • Architecture D A G I S Geospatial Semantic WS Provider Client Enforcement Module Decision Module Authorization Module Semantic-enabled Policy DB Web Service Client Side Web Service Provider Side

  17. Directions • Much of our research has focused on extending semantic web technologies for geospatial data interoperability • Longer term approach is to start from scratch and develop technologies specially for geospatial data • Security, privacy, misuse detection are all important considerations

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