Knowledge graph embedding aims at learning low-dimensional
representations for entities and relations in knowledge graph. Previous
knowledge graph embedding methods use just one score to measure the
plausibility of a fact, which can’t fully utilize the latent semantics of
entities and relations. Meanwhile, they ignore the type of relations in
knowledge graph and don’t use fact type explicitly. We instead propose
a model to fuse different scores of a fact and utilize relation and fact
type information to supervise the training process. Specifically, scores
by inner product of a fact and scores by neural network are fused with
different weights to measure the plausibility of a fact. For each fact,
besides modeling the plausibility, the model learns to classify different
relations and differentiate positive facts from negative ones which can be
seen as a muti-task method. Experiments show that our model achieves
better link prediction performance than multiple strong baselines on two
benchmark datasets WN18 and FB15k.