combining data mining and ontology engineering to enrich ontologies and linked data

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Presentation at the "first international workshop on Knowledge Discovery and Data Mining Meets Linked Open Data" (Know@LOD) at ESWC 2012


  • 1. Combining Data Mining and Ontology Engineering to enrich Ontologies and Linked DataMathieu dAquin Knowledge Media Institute (Kmi), The Open University, UK (@mdaquin) Gabriel KronbergerUniversity of Applied Science Upper Austria, School for Informatics, Communications and Media Mari Carmen Surez-Figueroa Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de Informtica, Universidad Politcnica de Madrid

2. The Knowledge Discovery Process 3. The Knowledge Discovery Process Ontology Patterns????? ?? populatedBy/modelling/characterising/structuring? Ontologies? 4. The Knowledge Discovery Process 5. The Knowledge Discovery Process 6. The Knowledge Discovery Process 7. The Ontology Engineering ProcessTraditionally In Linked Datacompetency through existing Ellicitatequestions, key Ellicitate domain informationknowledgeconcepts, etc. systems, etcModel diagrams, etc. Reuse fromfind commonlyknowledgeothersused vocabulariesRepresentalign, fill the gaps,OWL, RDFS, etc. Combineknowledgeetc.In both cases, it is expected that the data will somehow fitthe ontology, that the ontology will support relevantapplications, and support the inference of new information 8. Knowledge Engineering and KnowledgeDiscovery: a co-evolution process?Ellicitateknowledge/domain Modelknowledge/Reuse Represent knowledge/Combine Ontologies/ Knowledge Interpret Mine Data DataData Pre-process 9. Knowledge Engineering and KnowledgeDiscovery: a co-evolution process? 10. Major (new) issues 1/4Ontology-based filtering, checking andinterpretation of DM results Zablith et al., Using Ontological Contexts to Assess the Relevance of Statements in Ontology Evolution, EKAW 2010 Data DataDataText DocsAnalysisOntologiesMineRelationDiscovery New conceptsResults?? OntologyNew relations 11. Major (new) issues 2/4Mining from Linked and Ontology based dataNikolov et al., Unsupervised Learning ofLink Discovery Configuration, ESWC 2012OntologiesOntolo Ontolo gy gy DataDataDataDataData GeneticAlgorithmMine Similarity ConfigurationLink DiscoveryResults??Links 12. Major (new) issues 3/4Ontology-guided data miningdAquin and Motta, Extracting RelevantQuestions to an RDF Dataset Using FormalOntologiesConcept Analysis, K-CAP 2011Ontolo Inference + gyFormal ContextData Generation RDF Data MineFormal ContextProminent questions/queriesFormal Lattice ??InterprConcept Results etationAnalysis 13. Major (new) issues 4/4Versioning and consistencyRequires keeping trackof the different modelsand their versions, the Data Data agreement andDatadisagreementbetween them, as well OntologiesOntologiesas the areas ofMineMine Ontologies Mine?? consensus andcontroveries(dAquin, Formally MeasuringResult ResultAgreement and DisagreementssResultin Ontologies, K-CAP 2009)s Lead to the notion ofontologyconvergence 14. Conclusion Many existing works have considered theconnection between data mining and ontologyengineeing A large scale, web of linked data and ontologiesmake the related challenges more prominent and need real interactions between the twoapproaches, not as disconnected components. Need to investigate and exploit the colateralbenefits of ontology engineering and knowledgediscovery coming up with new techniques for enrichingknowledge from mined data, and guiding theextraction of further data wit ontological knowledge 15. Thank


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