A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)
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.
Cite
Text
Halliwell et al. "A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21618Markdown
[Halliwell et al. "A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/halliwell2022aaai-simplified/) doi:10.1609/AAAI.V36I11.21618BibTeX
@inproceedings{halliwell2022aaai-simplified,
title = {{A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)}},
author = {Halliwell, Nicholas and Gandon, Fabien and Lécué, Freddy},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {12963-12964},
doi = {10.1609/AAAI.V36I11.21618},
url = {https://mlanthology.org/aaai/2022/halliwell2022aaai-simplified/}
}