Based on prior knowledge, we can analyze the relationship between the occupation feature and the task of determining whether a person earns more than 50000 dollars per year.

From the given occupation categories, certain occupations are more likely to earn more than 50000 dollars per year compared to others. For example, occupations like 'Exec-managerial' and 'Prof-specialty' typically have higher earning potentials. On the other hand, occupations like 'Other-service' and 'Priv-house-serv' usually have lower earning potentials.

Based on this analysis, we can create a dictionary with the relevant occupation values for each target class:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Protective-serv", "?", "Other-service", "Craft-repair", "Adm-clerical", "Tech-support", "Sales", "Priv-house-serv", "Transport-moving", "Handlers-cleaners", "Armed-Forces"],
	"yes": ["Prof-specialty", "Exec-managerial"]
}
```

In this dictionary, the 'no' key represents the occupation values for individuals who do not earn more than 50000 dollars per year, while the 'yes' key represents the occupation values for individuals who earn more than 50000 dollars per year.