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December 2, 2013 Thessaloniki, Greece GNORASI WORKSHOP Charalampos Doulaverakis CERTH/ITI Knowledge and processing algorithms for remote sensing data Reasoning and semantic interpretation of visual data in GNORASI
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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 2
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Definition on ontology Representation and querying Land use/Land cover ontologies Ontologies and reasoning 3
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Ontology definition 4 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
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Ontology languages 5 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
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Rule languages 6 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, …
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Query languages 7 Query languages have been proposed for retrieving information from ontology repositories SPARQL, SeRQL, RDQL Most have similarities with SQL SELECT * WHERE {?X rdf:type gn:LandCover} Retrieve all instances of class gn:LandCover (SPARQL) Query languages can be used for deriving new facts CONSTRUCT {?region gn:depicts gn: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)
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Land Use/Land Cover systems 8 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 1 st level are broken down to 15 categories on the 2 nd level which in turn are broken down to 44 3 rd 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
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Knowledge-based analysis in GNORASI 9 Facts: large number of objects and fuzzy inference support Solution Ontology classification processor for rule definition Membership values are sent to the ontology Knowledge web service (java). Demonstrates the external use of processors Classification strategy (objects are always assigned to subclasses) Iterative execution of SPARQL UPDATE, GeoSPARQL Objects are classified to the class with highest membership value Example
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GNORASI classification 10 Challenges Solutions Usage examples Development details
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Challenges 11 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
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Solutions 12 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
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Rule definition UI 13 Hierarchy Class ruleset Rule definition
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Fuzzy values 14 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 Object idFuzzy Band1 Mean Fuzzy Roundness Fuzzy NDVI Fuzzy Band3 Kurtosis 10.3350.8430.7760.094 20.5980.3210.7050.544 30.1010.2020.7770.000 4 0.1011.0000.333
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Ontology data 15 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
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Example SPARQL Update 16 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:depictsClass gn:Sidewalk. gn:depiction1 gn:withConfidence ?conf. } WHERE {?object rdf:type gn:Object. ?object gn:fuzzyNDVIMean ?conf. } Vegetation INSERT {?object gn:depicts gn:depiction2. gn:depiction2 gn:depictsClass gn:Vegetation. gn:depiction2 gn:withConfidence ?conf2. } WHERE {?object rdf:type gn:Object. ?object gn:fuzzyNDVIMean ?conf2. } INSERT {?object gn:depicts gn:depiction1. gn:depiction1 gn:depictsClass gn:Sidewalk. gn:depiction1 gn:withConfidence ?conf. } WHERE {?object rdf:type gn:Object. ?object gn:fuzzyNDVIMean ?conf. ?filterObject rdf:type gn:Object. ?filterObject gn:depicts ?gn:filterDepiction. ?filterDepiction gn:depictsClass gn:Road FILTER (geof:sfTouches(?object, ?filterObject)) } Sidewalk rule with geospatial restriction (adjacent to Road )
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Classification 17 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:depictsClass gn:Sidewalk. gn:depiction1 gn:withConfidence ?conf. } WHERE {?object rdf:type gn:Object. ?object gn:fuzzyNDVIMean ?conf. ?filterObject rdf:type gn:Object. ?filterObject gn:depicts ?gn:filterDepiction. ?filterDepiction gn:depictsClass gn:Road FILTER (geof:touches(?object, ?filterObject)) }
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Classification 18 In a hierarchy, objects will try to match the deepest classes
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Classification 19 Object are assigned to classes with the highest membership value A minimum threshold is applied The service returns a list [ ]* This is the classification result
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Development 20 Java based REST communication Server Grizzly project 1 Reasoner employed OpenRDF Sesame 2 backend, OWLIM-lite 3 reasoner Geospatial library uSeekM IndexingSail 4 1 Grizzy, https://grizzly.java.net/https://grizzly.java.net/ 2 OpenRDF Sesame, http://www.openrdf.orghttp://www.openrdf.org 3 OWLIM Lite, http://www.ontotext.com/owlimhttp://www.ontotext.com/owlim 4 IndexingSail, https://dev.opensahara.com/projects/useekm/wiki/IndexingSailhttps://dev.opensahara.com/projects/useekm/wiki/IndexingSail
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Concluding 21 Efficient ontology-based classification Employ both feature-based classification and geospatial restrictions Handling of fuzzy membership values Built as a web service
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22 Thank you! Questions?
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