KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation

TL;DR

KGEx is a post-hoc explanation method for knowledge graph embeddings that uses surrogate models trained on sampled neighborhoods to identify influential training triples, enhancing interpretability of link predictions.

Contribution

Introduces KGEx, a novel approach that explains KGE predictions through surrogate models and knowledge distillation, improving interpretability of link prediction models.

Findings

KGEx provides faithful explanations aligned with black-box models.

Surrogate models effectively identify impactful training triples.

Method demonstrates robustness across multiple datasets.

Abstract

Despite being the go-to choice for link prediction on knowledge graphs, research on interpretability of knowledge graph embeddings (KGE) has been relatively unexplored. We present KGEx, a novel post-hoc method that explains individual link predictions by drawing inspiration from surrogate models research. Given a target triple to predict, KGEx trains surrogate KGE models that we use to identify important training triples. To gauge the impact of a training triple, we sample random portions of the target triple neighborhood and we train multiple surrogate KGE models on each of them. To ensure faithfulness, each surrogate is trained by distilling knowledge from the original KGE model. We then assess how well surrogates predict the target triple being explained, the intuition being that those leading to faithful predictions have been trained on impactful neighborhood samples. Under this assumption, we then harvest triples that appear frequently across impactful neighborhoods. We conduct extensive experiments on two publicly available datasets, to demonstrate that KGEx is capable of providing explanations faithful to the black-box model.