Different　brain　tumor　magnetic　resonance　imaging　(MRI)　modalities　provide　diverse　tumor-specific　information.　Previous　works　have　enhanced　brain　tumor　segmentation　performance　by　integrating　multiple　MRI　modalities.　However,　multi-modal　MRI　data　are　often　unavailable　in　clinical　practice.　An　incomplete　modality　leads　to　missing　tumor-specific　information,　which　degrades　the　performance　of　existing　models.　Various　strategies　have　been　proposed　to　transfer　knowledge　from　a　full　modality　network　(teacher)　to　an　incomplete　modality　one　(student)　to　address　this　issue.　However,　they　neglect　the　fact　that　brain　tumor　segmentation　is　a　structural　prediction　problem　that　requires　voxel　semantic　relations.　In　this　paper,　we　propose　a　Reconstruct　Incomplete　Relation　Network　(RIRN)　that　transfers　voxel　semantic　relational　knowledge　from　the　teacher　to　the　student.　Specifically,　we　propose　two　types　of　voxel　relations　to　incorporate　structural　knowledge:　Class-relative　relations　(CRR)　and　Class-agnostic　relations　(CAR).　The　CRR　groups　voxels　into　different　tumor　regions　and　constructs　a　relation　between　them.　The　CAR　builds　a　global　relation　between　all　voxel　features,　complementing　the　local　inter-region　relation.　Moreover,　we　use　adversarial　learning　to　align　the　holistic　structural　prediction　between　the　teacher　and　the　student.　Extensive　experimentation　on　both　the　BraTS　2018　and　BraTS　2020　datasets　establishes　that　our　method　outperforms　all　state-of-the-art　approaches.