This paper introduces a novel late interaction mechanism for knowledge base question answering (KBQA) systems, combining Graphormer and transformer representations. We conducted extensive experiments, comparing various pooling mechanisms and configurations. Our results demonstrate significant improvements in F1-score compared to traditional baselines. Specifically, we found that attention pooling, in conjunction with linearized graph and question features alongside sub-graph representations, yields the best performance. Our study highlights the importance of advanced interaction mechanisms and the integration of diverse modalities in KBQA systems.