Based on prior knowledge, it is reasonable to assume that whether a person owns a telephone could have an impact on their creditworthiness. Let's analyze the relationship between the `own_telephone` feature and the target variable.

To do this, we need data with the `own_telephone` feature and the corresponding target variable values (whether the person receives a credit or not). For each category of the `own_telephone` feature, we will examine the distribution of the target variable values and determine how they are related.

Let's assume we have the following data:

| own_telephone | credit |
|---------------|--------|
| yes           | yes    |
| none          | no     |
| none          | yes    |
| yes           | yes    |
| none          | no     |

Analyzing this data, we can observe that:

For the target class "yes":
- The possible values of the `own_telephone` feature are: ['yes'].

For the target class "no":
- The possible values of the `own_telephone` feature are: ['none'].

Based on this analysis, let's create the dictionary:

```json
{
	"yes": ["yes"],
	"no": ["none"]
}
```

This dictionary represents the relationship between the `own_telephone` feature and the target variable.