GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908485590974464 |
|---|---|
| author | Barile, Roberto d'Amato, Claudia Fanizzi, Nicola |
| author_facet | Barile, Roberto d'Amato, Claudia Fanizzi, Nicola |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs Barile, Roberto d'Amato, Claudia Fanizzi, Nicola Artificial Intelligence 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. |
| title | GRainsaCK: a Comprehensive Software Library for Benchmarking Explanations of Link Prediction Tasks on Knowledge Graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.08815 |