ProbaMap: a scalable tool for discovering probabilistic mappings between taxonomies

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.