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A Simplified Benchmark for Non-ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks

Nicholas Halliwell 1 Fabien Gandon 1 Freddy Lecue 2, 1 
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these black-box models. However, comparisons across explanation methods is difficult without a common dataset and standard evaluation metrics to evaluate the explanations. In this paper, we propose a method, including two datasets (Royalty-20k and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. We report the results of state-of-the-art explanation methods for RGCNs.
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Submitted on : Thursday, November 18, 2021 - 5:58:34 PM
Last modification on : Friday, August 5, 2022 - 3:50:42 AM

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  • HAL Id : hal-03339562, version 3

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Nicholas Halliwell, Fabien Gandon, Freddy Lecue. A Simplified Benchmark for Non-ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks. ISWC 2021 - International Semantic Web Conference, Oct 2021, Troy, United States. Proceedings of International Semantic Web Conference. ⟨hal-03339562v3⟩

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