GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs
This work addresses a gap for researchers in knowledge graph communities by offering a reusable tool for benchmarking explanation methods, though it is incremental as it builds on existing explanation techniques.
The authors tackled the lack of a standard evaluation protocol and benchmarking resource for explanations of link prediction tasks on knowledge graphs by proposing GRainsaCK, a comprehensive software library that streamlines model training and explanation evaluation, providing modularity and extensive documentation.
Since Knowledge Graphs are often incomplete, link prediction methods are adopted for predicting missing facts. Scalable embedding based solutions are mostly adopted for this purpose, however, they lack comprehensibility, which may be crucial in several domains. Explanation methods tackle this issue by identifying supporting knowledge explaining the predicted facts. Regretfully, evaluating/comparing quantitatively the resulting explanations is challenging as there is no standard evaluation protocol and overall benchmarking resource. We fill this important gap by proposing GRainsaCK, a reusable software resource that fully streamlines all the tasks involved in benchmarking explanations, i.e., from model training to evaluation of explanations along the same evaluation protocol. Moreover, GRainsaCK furthers modularity/extensibility by implementing the main components as functions that can be easily replaced. Finally, fostering its reuse, we provide extensive documentation including a tutorial.