A large-scale knowledge base can support a large number of practical applications, such as intelligent search and intelligent question answering. As the completeness of the information in a knowledge base may have a direct impact on the quality of downstream applications, its automatic completion has become a crucial task for many researchers and practitioners. To address this challenge, the knowledge representation learning technology which represents entities and relations as low-dimensional dense real value vectors has been developed rapidly in recent years. Although researchers continue to improve knowledge representation learning models using an increasingly complex feature engineering, we find that the most advanced models can be outdone by simply considering interactions from entities to relations and that from relations to entities without requiring huge number of parameters. In this work, we present a knowledge embedding model based on a bidirectional filter called TransBidiFilter. By learning the global shared parameter set based on the traditional gate structure, TransBidiFilter captures the restriction rules from entities to relations and that from relations to entities respectively. It achieves better automatic completion ability by modifying the standard translation-based loss function. In doing so, though with much fewer discriminate parameters, TransBidiFilter performs better than state-of-the-art baselines of semantic discriminate models on most indicators on many datasets.