Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
Akash Anil, Victor Gutierrez-Basulto, Yazmin Ibanez-Garcia, Steven Schockaert
Abstract
The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test graph. Rule-based methods seem like a natural fit for this task, but in practice they significantly underperform state-of-the-art methods based on Graph Neural Networks (GNNs), such as NBFNet. We hypothesise that the underperformance of rule-based methods is due to two factors: (i) implausible entities are not ranked at all and (ii) only the most informative path is taken into account when determining the confidence in a given link prediction answer. To analyse the impact of these factors, we study a number of variants of a rule-based approach, which are specifically aimed at addressing the aforementioned issues. We find that the resulting models can achieve a performance which is close to that of NBFNet. Crucially, the considered variants only use a small fraction of the evidence that NBFNet relies on, which means that they largely keep the interpretability advantage of rule-based methods. Moreover, we show that a further variant, which does look at the full KG, consistently outperforms NBFNet.- Anthology ID:
- 2024.lrec-main.792
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 9036–9049
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.792
- DOI:
- Cite (ACL):
- Akash Anil, Victor Gutierrez-Basulto, Yazmin Ibanez-Garcia, and Steven Schockaert. 2024. Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9036–9049, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis (Anil et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.792.pdf