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

5. XML Serialization of RDF Annotation Anonyms Resource

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… Description n -> 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. D 1 (Njal): Description

11. D 2 (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 S 1

14. D 1 (Njal): Concept Hierarchy

15. Hierarch Based on Descriptions of Length N Construct triples of intensions and related extensions Iteratively join triple L 1 with triple in path Join all possible triples and paths Construct intensions of length n Build sets S n from inclusion relations between node extensions

16. Building of S 2 from S 1

17. D 2 (Njal): Concept Hierarchy

18. Conclusion Lacks clarity Gaps in logic in explanation, S 1 -> 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