KGE-Path: Interpretable Link Prediction based on Knowledge Graph Embedding.

Explaining why a prediction is made is essential for the prediction to be trusted and acted upon. Most explainers of link predictions made by graph and knowledge-graph models are post-hoc: they treat the prediction model as a black box and perturb its input to obtain an explanation. This forces the model to run on inputs whose distribution differs from the one it was trained on, so the faithfulness of the explanation is assumed rather than guaranteed. It is also time-consuming, since an additional model has to be trained or optimized to produce the perturbation. To address both issues, we propose **KGE-Path**, a transparent knowledge graph embedding (KGE) model whose prediction relies only on the geometric relationship between the embeddings of the two nodes, together with an explanation method that is derived from this relationship. We exploit this transparency and score each path connecting the two query nodes using only the embeddings of the model and the topological structure of the input graph; the resulting paths, together with their scores, form an explanation that is read off directly from the model and therefore faithfully reflects how the prediction is made. To make the model better suited to this explanation, we also adapt the standard KGE training: an *edge penalty* that performs negative sampling over the relation types, a *node penalty* that exploits the information of the neighborhood, and a regularization term that further aligns the explanation with the prediction. On six datasets, KGE-Path explains better than GNN- and KGE-based explainers, and it explains a query pair faster than the baselines on four of the six datasets.