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Learning Ontologies from RDF Annotations

Learning Ontologies from RDF Annotations. Alexandre Delteil, Catherine Faron-Zucker, Rose Dieng ACACIA project, INRIA, 2004 Sophia Antipolis, France. TOC. Introduction RDF & RDFS Background Ontology Example Approach to Ontology Learning Conclusion Future Work. Introduction.

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Learning Ontologies from RDF Annotations

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  1. Learning Ontologies from RDF Annotations Alexandre Delteil, Catherine Faron-Zucker, Rose Dieng ACACIA project, INRIA, 2004 Sophia Antipolis, France

  2. TOC • Introduction • RDF & RDFS Background • Ontology Example • Approach to Ontology Learning • Conclusion • Future Work

  3. Introduction • Build ontologies from information extracted from RDF annotations “We have … a method to learn ontologies from RDF annotations by systematically generating the most specific generalization of all the possible sets of resources.”

  4. Property Resource or literal RDF Annotation • Triplet statement (resource, property, value), (Njal, type, Cat) • Easily represented as a graph • XML syntax provided

  5. Anonyms Resource XML Serialization of RDF Annotation <rdf:Description about=‘#Njal’> <rdf:type resource=‘#Cat’ /> <livesIn> <rdf:Description> <rdf:type resource=‘#House’ /> <ownedBy rdf:resouce=‘Catherine’ /> </rdf:Description> </livesIn> </rdf:Description>

  6. RDF Schema (RDFS) • RDFS -> schema specification language • Specifies ontological knowledge used in RDF statements • Consists of a set of declarations of classes and properties • Defines class and property hierarchies • Multiple inheritance

  7. RDFS Metamodel and RDFS Ontology

  8. Pieces of Knowledge and Descriptions • Piece of Knowledge -> set of nodes directly connected with the resource… • Descriptionn -> largest set of nodes connected with the resource and having a path length <= n • Complete Description -> the set of nodes connected to the resource through all possible properties

  9. Piece of Knowledge Relative to Njal

  10. D1(Njal): Description

  11. D2(Njal): Description

  12. Ontology Learning • Systematically consider all concepts covering a set of resource nodes • RDF graph resource extraction techniques preliminary first step • Group concepts and resources based on intensions and extensions • Incrementally build generalization hierarchy

  13. Building of S1

  14. D1(Njal): Concept Hierarchy

  15. Hierarch Based on Descriptions of Length N • Construct triples of intensions and related extensions • Iteratively join triple L1 with triple in path • Join all possible triples and paths • Construct intensions of length n • Build sets Sn from inclusion relations between node extensions

  16. Building of S2 from S1

  17. D2(Njal): Concept Hierarchy

  18. Conclusion • Lacks clarity • Gaps in logic in explanation, S1 -> Ontology • Relies on RDF annotations previously generated • Result complexity can increase exponentially • Requires no training data • Little or no user input • Implemented and tested inside European IST Comma Project

  19. Future Work • Inclusion of heuristics • Insertion of domain specific criteria • Graphical UI • Bounding methods to reduce complexity • RDF annotation generator

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