Computer Science > Artificial Intelligence
arXiv:2309.00306 (cs)
[Submitted on 1 Sep 2023]
Title:On the Aggregation of Rules for Knowledge Graph Completion
View a PDF of the paper titled On the Aggregation of Rules for Knowledge Graph Completion, by Patrick Betz and 3 other authors
View PDFAbstract:Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rulesets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.
| Comments: | KLR Workshop@ICML2023 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2309.00306 [cs.AI] |
| (orarXiv:2309.00306v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2309.00306 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled On the Aggregation of Rules for Knowledge Graph Completion, by Patrick Betz and 3 other authors
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