  N-ary relational knowledge bases (KBs) represent knowledge with binary and
beyond-binary relational facts. Especially, in an n-ary relational fact, the
involved entities play different roles, e.g., the ternary relation
PlayCharacterIn consists of three roles, ACTOR, CHARACTER and MOVIE. However,
existing approaches are often directly extended from binary relational KBs,
i.e., knowledge graphs, while missing the important semantic property of role.
Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM
for short, for facts in n-ary relational KBs. RAM explores a latent space that
contains basis vectors, and represents roles by linear combinations of these
vectors. This way encourages semantically related roles to have close
representations. RAM further introduces a pattern matrix that captures the
compatibility between the role and all involved entities. To this end, it
presents a multilinear scoring function to measure the plausibility of a fact
composed by certain roles and entities. We show that RAM achieves both
theoretical full expressiveness and computation efficiency, which also provides
an elegant generalization for approaches in binary relational KBs. Experiments
demonstrate that RAM outperforms representative baselines on both n-ary and
binary relational datasets.

Artificial Intelligence