Combining Data Mining and Ontology Engineering to enrich Ontologies and Linked Data

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

The Knowledge Discovery Process

The Knowledge Discovery Process Ontology Patterns? ?? ?? ?? populatedBy/modelling/characterising/structuring? Ontologies?

The Knowledge Discovery Process

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

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

Knowledge Engineering and KnowledgeDiscovery: a co-evolution process?

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

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

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

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

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

Thank you m.daquin@open.ac.ukhttp://people.kmi.open.ac.uk/mathieu @mdaquin

Combining Data Mining and Ontology Engineering to enrich Ontologies and Linked Data

by Mathieu d'Aquin on May 27, 2012

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