The inductive inference of the knowledge graph aims to complete the potential relations between the new unknown entities in the graph. Most existing methods are based on entity-independent features such as graph structure information and relationship information to inference. However, the neighborhood of these new entities is often too sparse to obtain enough information to build these features effectively. In this work, we propose a knowledge graph inductive inference method that fuses ontology information. Based on the enclosing subgraph, we bring in feature embeddings of concepts corresponding to entities to learn the semantic information implicit in the ontology. Considering that the ontology information of entities may be missing, we build a type constraint regular loss to explicitly model the semantic connections between entities and concepts, and thus capture the missing concepts of entities. Experimental results show that our approach significantly outperforms large language models like ChatGPT on two benchmark datasets, YAGO21K-610 and DB45K-165, and improves the MRR metrics by 15.4% and 44.1%, respectively, when compared with the state-of-the-art methods.