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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

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

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  • Is linear… a kit of stuff before you actually get to discover any thing1 start 1 stop
  • Replace the Database by a portion of the linked data set? The end product is an ontology?? That is populated by the data??? What are the intermediarry steps??? Ontology patterns? … and what…. And what… and what…Or is it that you have linked KD processes? (copy it and put links)
  • Replace the Database by a portion of the linked data set? The end product is an ontology?? That is populated by the data??? What are the intermediarry steps??? Ontology patterns? … and what…. And what… and what…Or is it that you have linked KD processes? (copy it and put links)
  • In any case, only displacing the problem…Linked data is “new”Linking processes to deal with it… not
  • How does that happen??? Not much….
  • I obtained this from DM, I have this ontology… what does it mean?Fouad’s work as preliminary example
  • Transcript

    • 1. Combining Data Mining and Ontology Engineering to enrich Ontologies and Linked Data Mathieu d’Aquin Knowledge Media Institute (Kmi), The Open University, UK (@mdaquin) Gabriel KronbergerUniversity of Applied Science Upper Austria, School for Informatics, Communications and Media Mari Carmen Suárez-Figueroa Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica 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 Data competency through existing Ellicitate questions, key Ellicitate domain information knowledge concepts, etc. systems, etc Model diagrams, etc. Reuse from find commonly knowledge others used vocabularies Represent align, fill the gaps, OWL, RDFS, etc. Combine knowledge etc.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 Model knowledge/Reuse Represent knowledge/Combine Ontologies/ Knowledge Interpret Mine Data Data Data 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 Data Data Text Docs Analysis Ontologies Mine Relation Discovery New concepts Results ?? Ontology New relations
    • 11. Major (new) issues 2/4Mining from Linked and Ontology based data Nikolov et al., Unsupervised Learning of Link Discovery Configuration, ESWC 2012 Ontologies Ontolo Ontolo gy gy Data Data Data Data Data Genetic Algorithm Mine Similarity Configuration Link Discovery Results ?? Links
    • 12. Major (new) issues 3/4Ontology-guided data mining d’Aquin and Motta, Extracting Relevant Questions to an RDF Dataset Using Formal Ontologies Concept Analysis, K-CAP 2011 Ontolo Inference + gy Formal Context Data Generation RDF Data Mine Formal Context Prominent questions/qu eries Formal Lattice ?? Interpr Concept Results etation Analysis
    • 13. Major (new) issues 4/4Versioning and consistency Requires keeping track of the different models and their versions, the Data Data agreement and Data disagreement between them, as well Ontologies Ontologies as the areas ofMine Mine Ontologies Mine ?? consensus and controveries (d’Aquin, Formally MeasuringResult Result Agreement and Disagreement s s Result in Ontologies, K-CAP 2009) s Lead to the notion of ontology convergence
    • 14. Conclusion• Many existing works have considered the connection between data mining and ontology engineeing• A large scale, web of linked data and ontologies make the related challenges more prominent…• … and need real interactions between the two approaches, not as disconnected components.• Need to investigate and exploit the colateral benefits of ontology engineering and knowledge discovery…• … coming up with new techniques for enriching knowledge from mined data, and guiding the extraction of further data wit ontological knowledge
    • 15. Thank you m.daquin@open.ac.ukhttp://people.kmi.open.ac.uk/mathieu @mdaquin