1. 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

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

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

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

5. Vision for Geospatial Semantic Web 0 GML, GML Schemas Rules/Query Logic, Proof and Trust TRUSTTRUST Other Services GRDF, Geospatial Ontologies Protocols PRIVACYPRIVACY 0 Adapted from Tim Berners Lee’s description of the Semantic Web

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

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

8. GRDF Example (Topology Ontology) <owl:minCardinality rdf:datatype="http://www.w3.org/2001/XMLSchema#int" >1 …

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

10. Geospatial Ontology l OWL-S for describing Geospatial Semantic Web Services l Developing Geospatial domain specific Ontology using OWL-DL for Geospatial Semantic Web services l Modular, Bottom-up Approach l Ontology shared between the Service Provider and Service Requestor l 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 l 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. l A single interface to search, retrieve and update the Geospatial data with secured end-to-end semantics. Map Result for ‘Between’ Geospatial Operator Query Results for ‘within’ Geospatial operator

12. Applications: Geospatial Data Interoperability l Current state-of-the-art is static or semi- automatic l On-the-fly “Knowledge Discovery” requires automated data integration techniques l Proposed solution:GML Schemas and OGC Standards solves Syntactic Heterogeneities l Semantics Issue: Existing frameworks lack data semantics. Solution needed! l 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. Data Mining: Ontology-Driven Classification Testing Image Pixels SVM Classifier Region Growing Shortest Path Tree Ontology Driven Rule Mining Classified Pixels Graph of Regions Graph of Near Neighboring Regions High Level Concept Training Image Pixels

14. Classification: Support Vector Machine (SVM) ClassifierTest set- 1 Test set- 2 ML90%40.09% SVM-Linear91%67.5% SVM-Polynomial89.3%50.7% SVM-RBF89.6%54.7% 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 l Architecture Client DAGISDAGIS DAGISDAGIS Geospatial Semantic WS Provider Enforcement Module Decision Module Authorization Module Semantic-enabled Policy DB Web Service Client SideWeb Service Provider Side

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