  Recent work in learning vector-space embeddings for multi-relational data has
focused on combining relational information derived from knowledge bases with
distributional information derived from large text corpora. We propose a simple
approach that leverages the descriptions of entities or phrases available in
lexical resources, in conjunction with distributional semantics, in order to
derive a better initialization for training relational models. Applying this
initialization to the TransE model results in significant new state-of-the-art
performances on the WordNet dataset, decreasing the mean rank from the previous
best of 212 to 51. It also results in faster convergence of the entity
representations. We find that there is a trade-off between improving the mean
rank and the hits@10 with this approach. This illustrates that much remains to
be understood regarding performance improvements in relational models.
