Based on the given feature, "education level", and the task of predicting whether a client subscribes to a term deposit or not, we need to analyze the relationship between education and the target variable.

By considering prior knowledge, it is possible that education level may impact a client's decision to subscribe to a term deposit. Higher education levels might be associated with higher incomes or financial knowledge, which in turn may increase the likelihood of subscribing to a term deposit. However, it is also important to note that this relationship may vary based on different factors such as the individual's occupation, age, and financial situation.

To create the dictionary, we need to analyze the distribution of education levels for both target classes, "yes" (client subscribed to a term deposit) and "no" (client did not subscribe to a term deposit). We will identify the possible values of education levels for each target class.

Here is the dictionary that represents the relationship:

```json
{
	"no": ["secondary", "unknown", "primary"],
	"yes": ["tertiary", "secondary", "unknown", "primary"]
}
```

In this case, the possible education levels for the "no" class are "secondary", "unknown", and "primary". For the "yes" class, the possible education levels are "tertiary", "secondary", "unknown", and "primary".