Based on prior knowledge, there is a possibility that having a telephone can be a relevant factor in determining whether a person receives credit or not. However, it is also possible that the presence or absence of a telephone may not have a significant impact on the decision.

To analyze the relationship between the "own_telephone" feature and the task of whether a person receives credit or not, we can examine the distribution of the "own_telephone" values for each target class.

Here is the dictionary representing the relationship between the "own_telephone" feature and the target variable:

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

In this case, we only have two possible values for the "own_telephone" feature: "yes" and "none". Based on the dictionary, it suggests that individuals who have a telephone ("own_telephone" = "yes") are more likely to receive credit (target class = "yes"). On the other hand, individuals who do not have a telephone ("own_telephone" = "none") are more likely to be denied credit (target class = "no").

Note that the dictionary only includes the distinct values observed in the dataset. If there were other values for the "own_telephone" feature not mentioned in the dataset, but with a considerable frequency, they would be included in the dictionary as well.