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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 cant fully utilize the latent semantics of entities and relations.Meanwhile,they ignore the type of relations in knowledge graph and dont 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.