(A,B),

-   pairs of objects for which one of the two objects occurs in the training dataset, like the pair (E,F),

-   pairs of objects for which none of the two objects is observed during training, like the pair (F,G).

The graphs on the right-hand side in Figure \[fig:examples\] show examples of specific types of relations that are covered by our framework. The differences between these relations will become more clear in the following sections.

![Left: example of a multi-graph representing the most general case, where no additional properties of relations are assumed. Right: examples of eight different types of relations in a graph of cardinality three. The following relational properties are illustrated: (C) crisp, (G) graded, (R) reciprocal, (S) symmetric, (T) transitive and (I) intransitive. For the reciprocal relations, (I) refers to a relation that does not satisfy weak stochastic transitivity, while (T) is showing an example of a relation fulfilling strong stochastic transitivity. For the symmetric relations, (I) refers a relation that does not satisfy $T$-transitivity w.r.t. the Łukasiewicz t-norm $T_{\bf L}(a,b) = \max(a+b-1,0)$, while (T) is showing an example of a relation that fulfills $T$-transitivity w.r.t. the product t-norm $T_{\bf P}(a,b) = ab$. See Section 4 for 