  Representation learning of knowledge graphs aims to embed entities and
relations into low-dimensional vectors. Most existing works only consider the
direct relations or paths between an entity pair. It is considered that such
approaches disconnect the semantic connection of multi-relations between an
entity pair, and we propose a convolutional and multi-relational representation
learning model, ConvMR. The proposed ConvMR model addresses the multi-relation
issue in two aspects: (1) Encoding the multi-relations between an entity pair
into a unified vector that maintains the semantic connection. (2) Since not all
relations are necessary while joining multi-relations, we propose an
attention-based relation encoder to automatically assign weights to different
relations based on semantic hierarchy. Experimental results on two popular
datasets, FB15k-237 and WN18RR, achieved consistent improvements on the mean
rank. We also found that ConvMR is efficient to deal with less frequent
entities.

Computation and Language;Artificial Intelligence