Based on prior knowledge, we can analyze the relationship between the feature "relationship" and the target variable "Does this person earn more than 50000 dollars per year?".

In this case, the feature "relationship" represents the relationship of the individual to others, with categories such as 'Own-child', 'Husband', 'Not-in-family', 'Unmarried', 'Wife', and 'Other-relative'. These categories indicate the familial or social relationship of the person.

To analyze the relationship between the feature and the target variable, we can look at the potential impact of the relationship on the income level. It is likely that the relationship feature can have an influence on the person's income. For example, individuals who are husbands or wives may have higher incomes compared to those who are not in a family or are unmarried.

Based on this analysis, we can create a dictionary as follows:

```json
{
    "no": ["Own-child", "Not-in-family", "Unmarried", "Other-relative"],
    "yes": ["Husband", "Wife"]
}
```

This dictionary indicates that for the target class "no" (man earning less than 50000 dollars per year), the possible values for the feature "relationship" are 'Own-child', 'Not-in-family', 'Unmarried', and 'Other-relative'. For the target class "yes" (man earning more than 50000 dollars per year), the possible values for the feature "relationship" are 'Husband' and 'Wife'.

Note that we do not include categories that are hard to predict or do not have enough representation in the dataset.