MultiConcept Alignment and Evaluation.ppt

Title of the presentation Multi-Concept Alignment and Evaluation Shenghui Wang, Antoine Isaac, Lourens van der Meij, Stefan Schlobach Ontology Matching Workshop Oct. 11th, 2007 Introduction: Multi-Concept Alignment Mappings involving combinations of concepts o1:FruitsAndVegetables → (o2:Fruits OR o2:Vegetables) Also referred to as: Multiple, complex Problem: only a few matching tools consider it Cf. [Euzenat Shvaiko] Why is MCA a Difficult Problem? Much larger search space: |O1| x |O2| → 2 |O1|x 2 |O2| How to measure similarity between sets of concepts? Based on which information and strategies? “Fruits and vegetables” vs. “Fruits” and “Vegetables” together Formal frameworks for MCA? Representation primitives owl:IntersectionOf? skosm:AND? Semantics A skos:broader ( skosm:AND B C) ? A broader B A broaderC ? Agenda The multi-concept alignment problem The Library case and the need for MCA Generating MCAs for the Library case Evaluating MCAs in the Library case Conclusion Yet MCA is needed in real-life problems KB collections (cf. OAEI slides) Scenario: re-annotation of GTT-indexed books by Brinkman concepts Yet MCA is needed in real-life problems Books can be indexed by several concepts with post-coordination: co-occurrence matters {G1=“History” , G2=“the Netherlands”} in GTT → a book about Dutch history Granularity of two vocabularies differ →{B1=“Netherlands; History”} Alignment should associate combination of concepts Agenda The multi-concept alignment problem The Library case and the need for MCA Generating MCAs for the Library case Evaluating MCAs in the Library case Conclusion MCA for Annotation Translation: Approach Produce similarity measures between individual concepts Sim(A,B) =X Grouping concepts based on their similarity {G1,B1,G2,G3,B2} Creating conversion rules {G1,G2,G3} → {B1,B2} Extraction of deployable alignment MCA Creation: Similarity Measures KB scenario has dually indexed books Brinkman and GTT concepts co-occur Instance-b