A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks

Nicholas Halliwell (1, 2, 3) , Fabien Gandon (1, 2, 3) , Freddy Lecue (1, 2, 3, 4)

1 Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
2 CRISAM - Inria Sophia Antipolis - Méditerranée
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
4 THALES [France]

Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]

Nicholas Halliwell, Fabien Gandon, Freddy Lecue. A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks. AAAI 2022 - 36th AAAI Conference on Artificial Intelligence, Feb 2022, Vancouver, Canada. ⟨hal-03434544v2⟩