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PARIS: Probabilistic Alignment of Relations, Instances, and Schema

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One of the main challenges that the SemanticWeb faces is the integration of a growing number of independently designed ontologies. In this work, we present PARIS , an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world’s largest ontologies.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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