## Horizontal Bar Chart: Model Performance Comparison (NTN vs RMNN)
### Overview
The chart compares the performance of two models, NTN (blue) and RMNN (red), across 14 relationship categories. Performance is measured on a scale from 60 to 100, with NTN consistently underperforming RMNN in most categories.
### Components/Axes
- **X-axis**: Performance Score (%) (60–100)
- **Y-axis**: Relationship categories (14 total)
- **Legend**:
- Blue = NTN
- Red = RMNN
- **Placement**: Legend is positioned on the right side of the chart.
### Detailed Analysis
1. **SymbolOf**: NTN (~60), RMNN (~80)
2. **DesireOf**: NTN (~85), RMNN (~75)
3. **CreatedBy**: NTN (~90), RMNN (~95)
4. **HasLastSubevent**: NTN (~85), RMNN (~88)
5. **Desires**: NTN (~75), RMNN (~80)
6. **CausesDesire**: NTN (~90), RMNN (~92)
7. **ReceivesAction**: NTN (~85), RMNN (~87)
8. **MotivatedByGoal**: NTN (~85), RMNN (~88)
9. **Causes**: NTN (~85), RMNN (~87)
10. **HasProperty**: NTN (~85), RMNN (~86)
11. **HasPrerequisite**: NTN (~85), RMNN (~88)
12. **HasSubevent**: NTN (~80), RMNN (~85)
13. **CapableOf**: NTN (~75), RMNN (~80)
14. **UsedFor**: NTN (~85), RMNN (~87)
### Key Observations
- **RMNN Dominance**: RMNN outperforms NTN in 12 out of 14 categories, with margins ranging from 2% (HasProperty) to 15% (SymbolOf).
- **NTN Weakness**: NTN’s lowest performance is in **SymbolOf** (~60), while RMNN’s weakest is **DesireOf** (~75).
- **Consistency**: RMNN maintains higher scores across all categories, with no overlap in performance ranges except for **DesireOf** and **CapableOf**.
### Interpretation
The data suggests RMNN is significantly more effective than NTN at modeling complex relationships, particularly in symbolic and causal contexts (e.g., **SymbolOf**, **CausesDesire**). NTN’s lower scores in **SymbolOf** and **Desires** may indicate limitations in handling abstract or indirect relationships. The minimal performance gap in **DesireOf** and **CapableOf** could reflect shared challenges in modeling desire-based or capability-based interactions. These results highlight RMNN’s architectural advantages in relationship extraction tasks.
