Based on prior knowledge, the feature "relationship" is likely to have some correlation with the target variable "earn more than 50000 dollars per year". Let's analyze the relationship between the feature and the target variable.

There are several possible relationships in the "relationship" feature: 
- Own-child: This category likely indicates that the individual is financially dependent on someone else and may have a lower chance of earning more than 50000 dollars per year.
- Husband: This category likely indicates that the individual is married and the primary earner in the family, which may increase the chance of earning more than 50000 dollars per year.
- Not-in-family: This category likely indicates that the individual is not living with any family members, which could have varying effects on their income. It's hard to make a definite prediction without additional information.
- Unmarried: This category likely indicates that the individual is not married and may not have the same financial stability as a married person. It may have a lower chance of earning more than 50000 dollars per year.
- Wife: This category likely indicates that the individual is married and may not be the primary earner in the family, which may decrease the chance of earning more than 50000 dollars per year.
- Other-relative: This category likely indicates that the individual is related to someone else but not in a direct family relationship. The effect on their income is hard to predict without additional information.

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

```json
{
	"no": ["Own-child", "Unmarried"],
	"yes": ["Husband", "Wife"]
}
```

We include the "Own-child" and "Unmarried" categories in the "no" list because they are more likely to have lower incomes. Similarly, the "Husband" and "Wife" categories are included in the "yes" list because they are more likely to have higher incomes.