Inductive knowledge graph completion has gained significant attention due to the dynamic nature of entities and facts in knowledge graphs (KGs). The goal of this task is to predict missing links between entities that are unseen during training. Graph neural networks (GNNs) have proven to be effective in handling this task. However, existing GNN-based methods overlook the type information of entities in KGs and thus may make incorrect predictions, which also limits the interpretability of the GNN-based models for KG completion. To address this limitation, we propose to incorporate type information into an existing GNN-based model for inductive KG completion. Experimental results show that our proposed approach is effective in improving the performance of inductive link prediction.