  Knowledge graph embedding aims to represent entities and relations in a
large-scale knowledge graph as elements in a continuous vector space. Existing
methods, e.g., TransE and TransH, learn embedding representation by defining a
global margin-based loss function over the data. However, the optimal loss
function is determined during experiments whose parameters are examined among a
closed set of candidates. Moreover, embeddings over two knowledge graphs with
different entities and relations share the same set of candidate loss
functions, ignoring the locality of both graphs. This leads to the limited
performance of embedding related applications. In this paper, we propose a
locally adaptive translation method for knowledge graph embedding, called
TransA, to find the optimal loss function by adaptively determining its margin
over different knowledge graphs. Experiments on two benchmark data sets
demonstrate the superiority of the proposed method, as compared to
the-state-of-the-art ones.
