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GNORASI workshop

The GNORASI workshop focuses on semantic interpretation of visual data through knowledge representation and reasoning algorithms. Key goals include the development of ontologies for image analysis, representation of expert knowledge, and creating reasoning processes to assist in image interpretation. The use of ontology languages like RDF, RDFS, and OWL enhances the ability to express complex relationships between classes, properties, and objects. Additionally, methodologies for handling large datasets and probabilistic inference methods are discussed, laying the groundwork for improved land use and cover classification in remote sensing.

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GNORASI workshop

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  1. Reasoning and semantic interpretation of visual data in GNORASI GNORASI workshop Knowledge and processing algorithms for remote sensing data Charalampos Doulaverakis CERTH/ITI

  2. Goals of semantic interpretation • Reasoning and representation of knowledge for semantic-enabled image analysis • Expert knowledge and visual information processing data are represented through ontologies • Development of a reasoning process for the knowledge assisted interpretation of images • Reasoning methods • Fuzzy inference support

  3. Ontologies and reasoning Definition on ontology Representation and querying Land use/Land cover ontologies

  4. Ontology definition • An ontology defines a set of representational primitives with which to model a domain of knowledge or discourse (Gruber, 1995) • Classes:which represent a set of objects • Properties: which express attributes and relations between classes and objects • Constraints: for expressing logical consistency • Individuals: atoms (objects) which are members of a class • Abstraction level of data models • Analogous to hierarchical and relational models • Ontologies, through inference, can provide us with implicitly defined information • Reasoning engines Thomas R. Gruber. Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal of Human-Computer Studies, vol. 43, no. 5-6, pages 907-928, 1995

  5. Ontology languages • Several languages have been proposed • RDF(S) • RDF: Uses URI for expressing relationships between objects (triples) • RDF: Allows structured and semi-structured data to be mixed, exposed, and shared across different applications. • RDFS: Allows the definition of classes, the relations between them and semantic constraints on RDF • OWL • Based on Description Logics. • Designed to represent rich and complex knowledge • 3 types of increasing expressivity • Lite, DL, Full • OWL2 • Logical extension of OWL • Deals with weaknesses in OWL expression and integrates features requested by users • 3 variations • EL, QL, RL

  6. Rule languages • They add expressive extensions to ontology languages • E.g: SandArea(?x), SeaArea(?y), isAdjacent(?x,?y)-> Beach(?x) • Standard languages have been defined • RuleML, SWRL • Other rule languages are offered by reasoning engines • Such as Jena, OWLIM, Pellet, Hermit, …

  7. Query languages • Query languages have been proposed for retrieving information from ontology repositories • SPARQL, SeRQL, RDQL • Most have similarities with SQL • SELECT * WHERE {?X rdf:typegn:LandCover} • Retrieve all instances of class gn:LandCover(SPARQL) • Query languages can be used for deriving new facts • CONSTRUCT {?region gn:depictsgn:Vegetation} WHERE { ?region gn:hasNDVI ?value . FILTER (?value > 0.5) } • A type of rule expression • Such approaches are proposed e.g. in SPIN (SPARQL Inference Notation)

  8. Land Use/Land Cover systems • Available land classifications correspond to specific applications, e.g. crop or vegetation characterization • As such, they cannot be used for generic telesensing applications • Most important of them are • CORINE • Organized in a 3 level hierarchy: 5 categories of the 1st level are broken down to 15 categories on the 2nd level which in turn are broken down to 44 3rd level categories • Land Cover Classification System (LCCS) • It doesn’t specify predefined land cover categories, it rather defines general classification criteria for characterizing land covers

  9. GNORASI classification Challenges Solutions Usage examples Development details

  10. Challenges • Object-based image analysis produces a large number of objects (thousands) • Probabilistic inference for class membership • Classification is based on user-defined rules • Feature-based • Geospatial restrictions • Classes can appear as premises in rules

  11. Solutions • Numerical computations are executed outside the ontological framework • Fuzzy membership values are computed using membership functions • Ontological inference is used for the assignment of objects to classes • According to user rules • SPARQL Update and GeoSPARQL are used to define the rules in ontological terms • Development decision • The ontological classification is implemented as an external web service

  12. Rule definition UI Class ruleset Hierarchy Rule definition

  13. Fuzzy values • The outcome of the rule definition processor are the fuzzy values of all objects for the features present in the rules • These arithmetic values have to be assigned to semantic entities, i.e. the defined classes

  14. Ontology data • The fuzzy values along with the class hierarchy and rule definitions are sent to the ontology classification service • The following are performed by the service • The class hierarchy is added to the core ontology • The fuzzy values are transformed to ontological data properties • Rules are translated to SPARQL Update queries • Rules are iteratively executed

  15. Example SPARQL Update • Example queries for assigning an object to classes Sidewalk and Vegetation with confidences ?conf1 and ?conf2 Sidewalk INSERT {?object gn:depicts gn:depiction1. gn:depiction1 gn:depictsClassgn:Sidewalk. gn:depiction1 gn:withConfidence?conf. } WHERE {?object rdf:typegn:Object. ?object gn:fuzzyNDVIMean?conf. } Vegetation INSERT {?object gn:depicts gn:depiction2. gn:depiction2 gn:depictsClassgn:Vegetation. gn:depiction2 gn:withConfidence?conf2. } WHERE {?object rdf:typegn:Object. ?object gn:fuzzyNDVIMean?conf2. } INSERT {?object gn:depicts gn:depiction1. gn:depiction1 gn:depictsClassgn:Sidewalk. gn:depiction1 gn:withConfidence?conf. } WHERE {?object rdf:typegn:Object . ?object gn:fuzzyNDVIMean?conf. ?filterObjectrdf:typegn:Object. ?filterObjectgn:depicts ?gn:filterDepiction. ?filterDepictiongn:depictsClassgn:Road FILTER (geof:sfTouches(?object, ?filterObject)) } Sidewalk rule with geospatial restriction (adjacent to Road)

  16. Classification • In the example, Sidewalk depends on Road definition. Objects assigned to Road must exist • Iterative rule execution until convergence (no changes in the repository) • In the end, every object will be assigned to classes with different membership values INSERT {?object gn:depicts gn:depiction1. gn:depiction1 gn:depictsClassgn:Sidewalk. gn:depiction1 gn:withConfidence ?conf. } WHERE { ?object rdf:typegn:Object. ?object gn:fuzzyNDVIMean ?conf. ?filterObjectrdf:typegn:Object. ?filterObjectgn:depicts ?gn:filterDepiction. ?filterDepictiongn:depictsClassgn:Road FILTER (geof:touches(?object, ?filterObject)) }

  17. Classification • In a hierarchy, objects will try to match the deepest classes

  18. Classification • Object are assigned to classes with the highest membership value • A minimum threshold is applied • The service returns a list [<object id> <class id> <confidence>]* • This is the classification result

  19. Development • Java based • REST communication • Server • Grizzly project1 • Reasoner employed • OpenRDF Sesame2 backend, OWLIM-lite3 reasoner • Geospatial library • uSeekM IndexingSail4 1Grizzy, https://grizzly.java.net/ 2OpenRDF Sesame, http://www.openrdf.org 3 OWLIM Lite, http://www.ontotext.com/owlim 4IndexingSail, https://dev.opensahara.com/projects/useekm/wiki/IndexingSail

  20. Concluding • Efficient ontology-based classification • Employ both feature-based classification and geospatial restrictions • Handling of fuzzy membership values • Built as a web service

  21. Thank you! Questions?

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