Role-Aware Modeling for N-ary Relational Knowledge Bases
This work addresses a limitation in knowledge representation for AI systems that handle complex relational data, offering an incremental improvement over existing methods.
The paper tackles the problem of modeling n-ary relational knowledge bases by incorporating role semantics, which existing methods often overlook, and shows that their Role-Aware Modeling (RAM) approach outperforms baselines on both n-ary and binary relational datasets.
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.