Existing methods for knowledge graph embedding do not ensure the high-rank triples predicted by themselves to be as consistent as possible with the logical background which is made up of a knowledge graph and a logical theory. Users must take great effort to filter consistent triples before adding new triples to the knowledge graph. To alleviate users' burden, we propose an approach to enhancing existing embedding-based methods to encode logical consistency into the learnt distributed representation for the knowledge graph, enforcing high-rank new triples as consistent as possible. To evaluate this approach, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets show that our approach is able to guarantee high-rank triples as consistent as possible while preserving a comparable performance as baseline methods in link prediction and triple classification. See readme.docx in the tool package for more details.