  To solve the inherent incompleteness of knowledge graphs (KGs), numbers of
knowledge graph completion (KGC) models have been proposed to predict missing
links from known triples. Among those, several works have achieved more
advanced results via exploiting the structure information on KGs with Graph
Convolutional Networks (GCN). However, we observe that entity embeddings
aggregated from neighbors in different directions are just simply averaged to
complete single-tasks by existing GCN based models, ignoring the specific
requirements of forward and backward sub-tasks. In this paper, we propose a
Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction
information, the multi-head self-attention is applied to specifically combine
embeddings in different directions based on various entities and sub-tasks, the
geometric constraints are imposed to adjust the distribution of embeddings, and
the traditional binary cross-entropy loss is modified to reflect the triple
uncertainty. Moreover, the competitive experiments results on several benchmark
datasets verify the effectiveness of our model.
