Anytime Bottom-Up Rule Learning for Knowledge Graph Completion
Open accessAbstract
We propose an anytime bottom-up technique for learning logical rules from large knowledge graphs. We apply the learned rules to predict candidates in the context of knowledge graph completion. Our approach outperforms other rule-based approaches and it is competitive with current state of the art, which is based on latent representations. Besides, our approach is significantly faster, requires less computational resources, and yields an explanation in terms of the rules that propose a candidate.
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For AnyBURL on datasets FB15k-237 and YAGO3-10, the paper used training time 1,000 secs instead of 10,000 secs because 10,000 secs made prediction times prohibitively long.
“We note that in AnyBURL [37], for datasets FB15k-237 and YAGO3-10 we used training time 1,000 secs whereas the original papers report slightly better results with a training time of 10000s; this is because, due to our already described necessity for full rankings, when using models trained for 10,000 secs prediction times got prohibitively long.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [37]
Section 5.1 reports AnyBURL results after both 1,000 and 10,000 seconds of rule learning and says that learning more rules increases application time. It does not say the original paper chose 1,000 seconds instead of 10,000 because prediction became prohibitively slow. In the citing sentence, that choice and its reason are described as the citing authors’ own. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAnyBURL is a very challenging baseline for LP, as it is shown to outperform most latent-features-based models.
“As a matter of fact, AnyBURL is a very challenging baseline for LP, as it is shown to outperform most latent-featuresbased models.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [37]
Section 5.2 says AnyBURL's results are at the same level as, and sometimes slightly better than, most of the embedding models compared. It reports stronger results than ConvE on most datasets and than SimplE, but says ComplEx-N3 performs better than AnyBURL. Thus the paper supports calling AnyBURL a challenging baseline, but 'outperform most' overstates its qualified comparison. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAnyBURL is computationally fast: depending on the training setting, it can take from a couple of minutes (100s setting) to about 3 hours (10000s setting) to learn its rules.
“It is also computationally fast: depending on the training setting, in order to learn its rules it can take from a couple of minutes ( 100s setting) to about 3 hours ( 10000s setting).”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [37]
The paper uses AnyBURL as a baseline.
“As a baseline we use AnyBURL [37], a popular LP model based on observable features.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [37]
In comparison to the embedding-based models, the baseline AnyBURL is strikingly fast.
“In comparison to the embedding-based models, the baseline project AnyBURL [37] is strikingly fast.”
From Knowledge Graph Embedding for Link Prediction (2021) · cites this paper as [37]
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Cites
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
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Cited by
Sorted by citations
Knowledge Graph Embedding for Link Prediction
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., & Merialdo, P. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3424672 · Cited by 601 · see citing works
“For the baseline AnyBURL [43], we compute prediction times a posteriori by dividing the time required to perform all test predictions by the number of test facts.”
BackgroundMethodInfluential
- Authors
- Meilicke, C., Chekol, M. W., Ruffinelli, D., & Stuckenschmidt, H.
- Affiliations
- University of Mannheim (DE)
- Venue
- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
- Published
- 2019-08
- Pages
- 3137-3143
- OpenAlex
- W2966298461
- Cited by
- 252
- References
- 2
- Citations by year
- 2026: 22 · 2025: 38 · 2024: 51 · 2023: 57 · 2022: 36 · 2021: 26 · 2020: 20 · 2019: 2
- FWCI
- 8.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- MADOC (University of Mannheim) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- knowledge graph completion, anytime algorithms, rule-based explanations
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