Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become the mainstream of SBRs benefited from the superiority of GNN in modeling complex dependencies. Based on a strong assumption of adjacent dependency, any two adjacent items in a session are necessarily dependent in most GNN-based SBRs. However, we argue that due to the uncertainty and complexity of user behaviors, adjacency does not necessarily indicate dependency. However, the above assumptions do not always hold in actual recommendation scenarios, so it can easily lead to two drawbacks: (1) false dependencies occur in the session because there are adjacent but not really dependent items, and (2) the missing of true dependencies occur in the session because there are non-adjacent but actually dependent items. These drawbacks significantly affect item representation learning, degrading the downstream recommendation performance. To address these deficiencies, we propose a novel review-refined inter-item graph neural network (RI-GNN), which utilizes topic information extracted from the reviews of items to improve dependencies between items. Experiments on two public real-world datasets demonstrate that RI-GNN outperforms SOTA methods.