Based on prior knowledge, the ST_Slope feature represents the slope of the peak exercise ST segment. It is a categorical variable with three possible values: 'Up', 'Flat', and 'Down'. To determine its relationship with the presence of heart disease, we need to analyze the distribution of ST_Slope values for each target class (yes and no).

Let's assume we have a dataset with the following distribution:

| ST_Slope |   Target   |
|:--------:|:----------:|
|   Flat   |    Yes     |
|   Flat   |    No      |
|    Up    |    Yes     |
|   Down   |    Yes     |
|   Down   |    No      |
|   Down   |    No      |

Based on this dataset, we can create the following dictionary:

```json
{
	"no": ["Down"], 
	"yes": ["Flat", "Up", "Down"]
}
```

In this case, the only ST_Slope value observed for the "no" target class is "Down". For the "yes" target class, we observed all three ST_Slope values: "Flat", "Up", and "Down". The other missing values are not included in the dictionary as they were not observed in the dataset.

Please note that this analysis is hypothetical and based on the limited data presented above. The actual relationship between ST_Slope and heart disease would require a more comprehensive analysis of a larger dataset in a real-world scenario.