Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
This addresses the problem of incomplete knowledge graphs for AI systems requiring logical reasoning, offering an interpretable and more expressive method, though it is incremental as it builds on existing rule learning approaches.
The paper tackles the limitation of existing probabilistic logical rule learning methods in knowledge graph completion, which overlook contextual information from neighboring sub-graphs, by proposing Logical Entity Representation (LERP) to encode this information, resulting in outperforming other rule learning methods and achieving comparable or superior performance to state-of-the-art black-box methods.
Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as $R_1(x,z)\land R_2(z,y)\Rightarrow H(x,y)$. This formulation overlooks additional contextual information from neighboring sub-graphs of entity variables $x$, $y$ and $z$. Intuitively, there is a large gap here, as local sub-graphs have been found to provide important information for knowledge graph completion. Inspired by these observations, we propose Logical Entity RePresentation (LERP) to encode contextual information of entities in the knowledge graph. A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph. It is an interpretable representation while allowing for differentiable optimization. We can then incorporate LERP into probabilistic logical rule learning to learn more expressive rules. Empirical results demonstrate that with LERP, our model outperforms other rule learning methods in knowledge graph completion and is comparable or even superior to state-of-the-art black-box methods. Moreover, we find that our model can discover a more expressive family of logical rules. LERP can also be further combined with embedding learning methods like TransE to make it more interpretable.