A Probabilistic Approach to Knowledge Translation
  In this paper, we focus on a novel knowledge reuse scenario where the
knowledge in the source schema needs to be translated to a semantically
heterogeneous target schema. We refer to this task as "knowledge translation"
(KT). Unlike data translation and transfer learning, KT does not require any
data from the source or target schema. We adopt a probabilistic approach to KT
by representing the knowledge in the source schema, the mapping between the
source and target schemas, and the resulting knowledge in the target schema all
as probability distributions, specially using Markov random fields and Markov
logic networks. Given the source knowledge and mappings, we use standard
learning and inference algorithms for probabilistic graphical models to find an
explicit probability distribution in the target schema that minimizes the
Kullback-Leibler divergence from the implicit distribution. This gives us a
compact probabilistic model that represents knowledge from the source schema as
well as possible, respecting the uncertainty in both the source knowledge and
the mapping. In experiments on both propositional and relational domains, we
find that the knowledge obtained by KT is comparable to other approaches that
require data, demonstrating that knowledge can be reused without data.
