  This article firstly attempts to explore parallel algorithms of learning
distributed representations for both entities and relations in large-scale
knowledge repositories with {\it MapReduce} programming model on a multi-core
processor. We accelerate the training progress of a canonical knowledge
embedding method, i.e. {\it translating embedding} ({\bf TransE}) model, by
dividing a whole knowledge repository into several balanced subsets, and
feeding each subset into an individual core where local embeddings can
concurrently run updating during the {\it Map} phase. However, it usually
suffers from inconsistent low-dimensional vector representations of the same
key, which are collected from different {\it Map} workers, and further leads to
conflicts when conducting {\it Reduce} to merge the various vectors associated
with the same key. Therefore, we try several strategies to acquire the merged
embeddings which may not only retain the performance of {\it entity inference},
{\it relation prediction}, and even {\it triplet classification} evaluated by
the single-thread {\bf TransE} on several well-known knowledge bases such as
Freebase and NELL, but also scale up the learning speed along with the number
of cores within a processor. So far, the empirical studies show that we could
achieve comparable results as the single-thread {\bf TransE} performs by the
{\it stochastic gradient descend} (SGD) algorithm, as well as increase the
training speed multiple times via adapting the {\it batch gradient descend}
(BGD) algorithm for {\it MapReduce} paradigm.
