1. Semantic Interoperability in GIS N. L. Sarda Suman Somavarapu

2. The Need for Interoperability Many sources are collecting and storing geographic data. Many sources are collecting and storing geographic data. Effective utilization only if data is sharable and interoperable. Effective utilization only if data is sharable and interoperable. Existing Solution Open standards for interoperation of GIS. Open standards for interoperation of GIS. GML - Data interchange standard for geographic data. GML - Data interchange standard for geographic data. WFS, WMS - Geospatial Web Service interfaces. WFS, WMS - Geospatial Web Service interfaces. Does not address Semantic heterogeneities. Does not address Semantic heterogeneities.

3. The Semantics Problem Different entities names same meaning. Different entities names same meaning. Same entities name different meaning. Same entities name different meaning. Different classification hierarchies. Different classification hierarchies.

4. Our Solution - SANGAM SemANtic Geographic data Access Methodology Use Ontologies to capture the semantics of the information and to make the content explicit Use Ontologies to capture the semantics of the information and to make the content explicit Identify and associate semantically corresponding information concepts. (Ontology Mapping) Identify and associate semantically corresponding information concepts. (Ontology Mapping) Check consistency of the mappings using ontology reasoners. (Mapping Validation). Check consistency of the mappings using ontology reasoners. (Mapping Validation).

5. Our Solution - SANGAM Use the Ontology Mappings to generate XQuery transformations for data conversion. (Wrapper Generation). Use the Ontology Mappings to generate XQuery transformations for data conversion. (Wrapper Generation). Accept data requests, transform them according to sources. Accept data requests, transform them according to sources. Apply wrappers on the data got from sources and return merged data (Data Extraction). Apply wrappers on the data got from sources and return merged data (Data Extraction).

6. Outline Ontologies and Ontology Mapping Ontologies and Ontology Mapping Ontology Reasoning Ontology Reasoning Wrapper Generation Wrapper Generation Data Extraction Data Extraction Results and Analysis Results and Analysis

7. Ontologies An explicit specification of a shared conceptualization. An explicit specification of a shared conceptualization. Defined using the concepts of Defined using the concepts of Classes Classes Data and Object Properties Data and Object Properties Constraints and Axioms Constraints and Axioms

8. Geographic Ontology Model

9. Ontology Mapping Reconciling differences between heterogeneous ontologies Class Level: Equivalent, Subclass, Superclass etc Class Level: Equivalent, Subclass, Superclass etc Eg: Student –> Graduate Student Eg: Student –> Graduate Student Data Properties: Equivalence, Aggregation, Concatenation, Transformation etc Data Properties: Equivalence, Aggregation, Concatenation, Transformation etc Eg: RoadLength(in Km)= RoadLength(in m)*1000. Eg: RoadLength(in Km)= RoadLength(in m)*1000. Name  Firstname. Lastname Name  Firstname. Lastname Object Properties: Equivalence, Union, Categorization Object Properties: Equivalence, Union, Categorization

10. Mapping Process 1. Generation of lexical similarity Parts of speech tagging Parts of speech tagging Semantic similarity between two words Semantic similarity between two words Semantic similarity between two descriptions Semantic similarity between two descriptions 2. Propagation of superclass and attribute similarity 3. Propagation of subclass similarity 4. Similar mappings.

11. Generation of lexical similarity WordNet - Semantic relations A lexical reference system. A lexical reference system. Nouns, verbs, adjectives and adverbs are organized into synonym sets. Nouns, verbs, adjectives and adverbs are organized into synonym sets. Each synonym set or synset represents one underlying lexical concept. Each synonym set or synset represents one underlying lexical concept. Synsets are connected to one another through the explicit semantic relations. Synsets are connected to one another through the explicit semantic relations.

12. User Interaction in Ontology Mapping

13. Layer Level Mapping

14. Attribute Level Define Transformations

15. Mapping Validation 1. Convert Ontology to OWL using XSLT 2. Use JENA Model Factory to generate a model instance 3. Set an external OWL reasoner (Pellet) 4. Provides inconsistencies as output.

16. OWL and Jena OWL- Web Ontology Language standard for representing OWL- Web Ontology Language standard for representing ontologies in machine readable form. ontologies in machine readable form. Constructs of classes and properties like Class, subClassOf, Constructs of classes and properties like Class, subClassOf, Property, subPropertyOf, equivalentClass, equivalentProperty Property, subPropertyOf, equivalentClass, equivalentProperty Jena is a Java framework for building Semantic Web applications. Jena is a Java framework for building Semantic Web applications. Provides a programmatic environment for RDF, RDFS and OWL. Provides a programmatic environment for RDF, RDFS and OWL. Includes a rule-based inference engine. External reasoners can be plugged in. Includes a rule-based inference engine. External reasoners can be plugged in.

17. Converting the Ontologies to OWL 1. Create owl:Ontology element with the rdf:Label as Source name. <rdfs:label>Transportation</rdfs:label></owl:Ontology> 2. For each class which doesn’t have any parent 3. For each class which has a parent </owl:Class>

18. Converting the Ontologies to OWL For each Data Property For each Data Property </owl:DatatypeProperty> For each Object Property For each Object Property </owl:ObjectProperty>

19. Converting the Ontology Mappings to OWL Create a owl:Ontology element with the imports for Source and Target ontology. Create a owl:Ontology element with the imports for Source and Target ontology. Integration of OGC and Ordnance Survey Integration of OGC and Ordnance Survey </owl:Ontology> For each class in the source ontology get it mappings For each class in the source ontology get it mappings If the mapping is of the type equivalent then If the mapping is of the type equivalent then </owl:Class>

20. Converting the Ontology Mappings to OWL 1. If the mapping is of the type SubClassOf then </owl:Class> 2. If the mapping is of type SuperClassOf then </owl:Class>

21. Converting the Ontology Mappings to OWL 1. For each Data Property in the source ontology </owl:DatatypeProperty> 2. For each Object Property in source ontology </owl:ObjectProperty>

22. Wrapper Generation Ontology Mappings can be seen as abstract specification of relationships between ontology entities Ontology Mappings can be seen as abstract specification of relationships between ontology entities Wrapper gives the rules for converting the data according to one schema into that of according to another schema. Wrapper gives the rules for converting the data according to one schema into that of according to another schema. Data from different sources will be in GML form, which is XML based. Data from different sources will be in GML form, which is XML based. So, we generate XQuery transformations for achieving this. So, we generate XQuery transformations for achieving this.

23. SourceOntologyTargetOntology Ontology Mapper Data 1 Data 2 Wrapper

24. Onto1.feature1 == Onto2.feature1 Onto1.feature1 == Onto2.feature2 Onto1.featureM == Onto2.featureN....... Rules written in xQuery format Wrapper Generation Rules generated by semantic matching (semi-automatic)

25. Attribute - Simple Equivalence XQuery Template

26. Attribute - Transformation XQuery Template

27. Class - Simple Equivalence XQuery Template

28. Class - Subclass XQuery Template

29. Class - Superclass XQuery Template

30. Wrapper Generation Start with Source node call the RecursiveXQueryGen If Source 1 Get all First level layers 2 Get all direct Classes of the source 3 Start the XQuery template for Source 4 Take each layer collected and recurse 5 Take each class collected and recurse 6 End template for Source

31. Wrapper Generation If Layer 1. Get all its Sub layers 2. Get all its Classes 3. Check for its Mappings 4. Start the xQuery template for each layer accordingly 5. Take each layer collected and recurse 6. Take each class collected and recurse 7. End template for Layer

32. Wrapper Generation If Class 1. Get all its Sub Classes 2. Get all its Attributes 3. Check for its Mappings 4. Start xQuery template for each class accordingly 5. Take each attribute collected and recurse 6. End template for Class 7. Take each class collected and recurse

33. Wrapper Generation If Attribute 1. Get the Mapping type 2. Process the User Transformation 3. Generate the XQuery accordingly

34. Data1, Data2 (Changes the structure of data) Wrapper Source Ontology Source Data Target Ontology Source Data Data Extraction

35. Data Extraction involves getting the actual data from the Data Extraction involves getting the actual data from the data sources and then applying the transformation. We provide an interface for querying the data at feature level. We provide an interface for querying the data at feature level. For the features requested, their mappings are identified and then WFS Requests are generated for different data sources to get the GML data. For the features requested, their mappings are identified and then WFS Requests are generated for different data sources to get the GML data. Transformations are applied on the data got from different sources and the merged data is given to the user. Transformations are applied on the data got from different sources and the merged data is given to the user.

36. Data Request and its Transformation Data request will consists of a) list of features b) Coordinates of bounding box Identify the data sources with which the data consumer’s ontology is mapped. For each data source with which there exists a ontology mapping, Check mappings for the features in the data request. Add all the features in mappings for that particular source.

37. Experiments Experiments using ontologies of Ordnance Survey and OGC for the transportation domain Experiments using ontologies of Ordnance Survey and OGC for the transportation domain Lexical and Propagated similarity values are taken and compared with human generated values Lexical and Propagated similarity values are taken and compared with human generated values For human generated similarity values three Scores for every pair of classes For human generated similarity values three Scores for every pair of classes Based on English meaning of the words. Based on English meaning of the words. Reflexive of Superclass similarity Reflexive of Superclass similarity Reflexive of Subclass and attribute similarity Reflexive of Subclass and attribute similarity

38. Analysis False Positives: (the error of rejecting something that should have been accepted; e.g., such as finding an innocent person guilty) False Positives: (the error of rejecting something that should have been accepted; e.g., such as finding an innocent person guilty) 12.3% at t threshold of 0.50 12.3% at t threshold of 0.50 36.9% at a threshold of 0.40. 36.9% at a threshold of 0.40. Mainly seen in the cases where parts of the target class name existed as a part of the source class name. Mainly seen in the cases where parts of the target class name existed as a part of the source class name. False Negatives: (the error of not rejecting something that should have been rejected; e.g., such as finding a guilty person innocent) False Negatives: (the error of not rejecting something that should have been rejected; e.g., such as finding a guilty person innocent) 4 percent at a threshold of 0.30 4 percent at a threshold of 0.30 (25%) in the top 20 class matches based on human generated similarity values. (25%) in the top 20 class matches based on human generated similarity values.

39. Analysis

40. Conclusion Semantic Heterogeneity is the key challenges for GIS Interoperability. Lexical similarities of schema element labels and descriptions can help in ontology mapping. Similarity propagation based on heuristics allows integration of implicit semantics of the ontology structure and hence improves the mapping. Mapping Validation, Wrapper Generation, Data Extraction components further help in having a End-to- End framework for GIS Interoperability

41. Thank You Thank You