Knowledge graph embedding (KGE) are routinely used to represent entities and their relations in knowledge bases with a quantitative measure, and the triples usually play the role of basic units in KGE learning. Considering that triples are sometimes far from adequate and knowledge graph itself contains a lot of information, this paper employs FCA-based technology to mine the deterministic knowledge from knowledge graph, that is, the formal concept, and attempts to establish the relationship between knowledge graph (KG) and formal concept analysis (FCA). Specically, each set of triples sharing the same head entity are grouped as a graph granule and the concepts of each graph granule are mined. By further exploration, the maximal concepts are integrated into embedding learning to develop a novel KGE model named TransGr for knowledge graph completion. This model learns a matrix for each maximal concept in graph granule as well as a vector for each entity and relation. The performed experiments on link prediction and triple classi cation tasks demonstrate that the proposed TransGr model is effective on the datasets with relatively complete graph granules.