Based on prior knowledge, there might be a relationship between the type of chest pain (feature: ChestPainType) and the presence of heart disease (target: coronary angiography showing heart disease).

To determine this relationship, we can analyze the distribution of ChestPainType values for each target class. Let's assume "yes" represents the presence of heart disease and "no" represents the absence of heart disease.

Here's the dictionary that represents the relationship between the feature and target:

```json
{
	"no": ["NAP", "TA"],  
	"yes": ["ATA", "ASY"]  
}
```

Analysis:
- For the target class "no" (no heart disease), the possible values for ChestPainType are "NAP" and "TA".
- For the target class "yes" (presence of heart disease), the possible values for ChestPainType are "ATA" and "ASY".

Please note that this analysis is based on the assumption that there are only four possible categories for the ChestPainType feature: "ATA", "NAP", "ASY", and "TA". If there are additional categories not mentioned in the feature description, the analysis and resulting dictionary might need to be updated accordingly.