Learning Ontologies from RDF Annotations

Alexandre Delteil, Catherine Faron-Zucker, Rose Dieng

ACACIA project, INRIA, 2004Sophia Antipolis, France

TOC

IntroductionRDF & RDFS BackgroundOntology ExampleApproach to Ontology LearningConclusionFuture Work

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.”

RDF Annotation

Triplet statement (resource, property, value), (Njal, type, Cat)Easily represented as a graphXML syntax provided

Resource or literalProperty

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>

Anonyms Resource

RDF Schema (RDFS)

RDFS -> schema specification languageSpecifies ontological knowledge used in RDF statementsConsists of a set of declarations of classes and propertiesDefines class and property hierarchiesMultiple inheritance

RDFS Metamodel and RDFS Ontology

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 <= nComplete Description -> the set of nodes connected to the resource through all possible properties

Piece of Knowledge Relative to Njal

D1(Njal): Description

D2(Njal): Description

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 extensionsIncrementally build generalization hierarchy

Building of S1

D1(Njal): Concept Hierarchy

Hierarch Based on Descriptions of Length N

Construct triples of intensions and related extensionsIteratively join triple L1 with triple in pathJoin all possible triples and pathsConstruct intensions of length n Build sets Sn from inclusion relations between node extensions

Building of S2 from S1

D2(Njal): Concept Hierarchy

Conclusion

Lacks clarityGaps in logic in explanation, S1 -> OntologyRelies on RDF annotations previously generatedResult complexity can increase exponentiallyRequires no training dataLittle or no user inputImplemented and tested inside European IST Comma Project

Future Work

Inclusion of heuristicsInsertion of domain specific criteriaGraphical UIBounding methods to reduce complexityRDF annotation generator