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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 
Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03434544
Contributor : Nicholas Halliwell Connect in order to contact the contributor
Submitted on : Wednesday, December 1, 2021 - 3:55:53 PM
Last modification on : Friday, August 5, 2022 - 3:50:57 AM

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  • HAL Id : hal-03434544, version 2

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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⟩

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