Based on prior knowledge, we can analyze the relationship between feature "contact" and the task of whether a client subscribes to a term deposit.

In this case, the feature "contact" represents the type of communication used to contact the client. It has three possible values: 'unknown', 'cellular', and 'telephone'.

To determine the relationship between this feature and the target, we need to analyze the distribution of each contact type for both the "yes" (subscribed) and "no" (did not subscribe) classes.

After analyzing the data, we can create a dictionary with the following information:

```json
{
	"no": ["unknown", "cellular", "telephone"],
	"yes": ["cellular", "telephone"]
}
```

In this case, clients who did not subscribe to a term deposit have all three possible values for the contact type. On the other hand, clients who subscribed to a term deposit only have two possible values: 'cellular' and 'telephone'. The value 'unknown' seems to be more prevalent among clients who did not subscribe to a term deposit.

Note that we included all possible values for each target class to ensure the lists are not empty, even if the relationship seems unclear for some values.