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ProbaMap: a scalable tool for discovering probabilistic mappings between taxonomies
This video was recorded at 1st Workshop on Automated Knowledge Based Construction (AKBC), Grenoble 2010. In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm (called ProbaMap) which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
