An Explainable Path Reasoning Framework for Knowledge Graph Completion

Knowledge Graphs (KGs) are vital for structured knowledge but suffer from intrinsic incompleteness. Knowledge Graph Completion (KGC) faces a critical trade-off between the performance of ”black-box” models and the interpretability of explainable approaches that rely on purely symbolic structures. To resolve this, we propose the Explainable Path Reasoning (EPR) framework, which synergizes statistical topology analysis with deep semantic modeling. EPR first mines a corpus of statistically-grounded reasoning paths using a hop-normalized conditional likelihood to mitigate length bias. This corpus is then used to train a powerful BERT encoder via a path-level contrastive objective, teaching it to comprehend compositional semantics. For inference, a single, unified BERT architecture powers a dynamic beam search, performing the dual role of scoring candidates for prediction and constructing faithful, multi-hop explanations. Experimental results demonstrate that EPR establishes a new competitive performance among explainable methods and significantly narrows the performance gap to leading black-box models, providing a powerful and transparent solution for KGC, which is essential for trustworthy decision-making in intelligent communication systems.