  Several recently proposed methods aim to learn conceptual space
representations from large text collections. These learned representations
asso- ciate each object from a given domain of interest with a point in a
high-dimensional Euclidean space, but they do not model the concepts from this
do- main, and can thus not directly be used for catego- rization and related
cognitive tasks. A natural solu- tion is to represent concepts as Gaussians,
learned from the representations of their instances, but this can only be
reliably done if sufficiently many in- stances are given, which is often not
the case. In this paper, we introduce a Bayesian model which addresses this
problem by constructing informative priors from background knowledge about how
the concepts of interest are interrelated with each other. We show that this
leads to substantially better pre- dictions in a knowledge base completion
task.
