Abstract: Knowledge graph (KG) breaks the data isolation in different scenarios and provides basic support for the practical application. The representation learning transforms KG into the low-dimensional vector space to facilitate KG application. However, there are two problems in KG representation learning: 1)It is assumed that KG satisfies the closed-world assumption. It requires all entities to be visible during the training. In reality, most KGs are growing rapidly, e.g. a rate of 200 new entities per day in the DBPedia. 2)Complex semantic interaction, such as matrix projection and convolution, are used to improve the accuracy of the model and limit the scalability of the model. To this end, we propose a representation learning method TransNS for open KG that allows new entities to exist. It selects the related neighbors as the attribute of the entity to infer the new entity, and uses the semantic affinity between the entities to select the negative triple in the learning phase to enhance the semantic interaction capability. We compare our TransNS with the state-of-the-art baselines on 5 traditional and 8 new datasets. The results show that our TransNS performs well in the open KGs and even outperforms existing models on the baseline closed KGs.