Papers › How to Turn Your Knowledge Graph Embeddings into Generative Models
How to Turn Your Knowledge Graph Embeddings into Generative Models
Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari
Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this perspective they are not amenable for exact maximum-likelihood estimation (MLE), sampling and struggle to integrate logical constraints. This work re-interprets the score functions of these KGEs as circuits -- constrained computational graphs allowing efficient marginalisation. Then, we design two recipes to obtain efficient generative circuit models by either restricting their activations to be non-negative or squaring their outputs. Our interpretation comes with little or no loss of performance for link prediction, while the circuits framework unlocks exact learning by MLE, efficient sampling of new triples, and guarantee that logical constraints are satisfied by design. Furthermore, our models scale more gracefully than the original KGEs on graphs with millions of entities.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-biokg | ComplEx^2 | Ext. data | No | #4 of 16 | Archive leaderboard | report |
| Link Property Prediction | ogbl-biokg | ComplEx^2 | Number of params | 187648000 | #4 of 16 | Archive leaderboard | report |
| Link Property Prediction | ogbl-biokg | ComplEx^2 | Test MRR | 0.8583 ± 0.0005 | #4 of 16 | Archive leaderboard | report |
| Link Property Prediction | ogbl-biokg | ComplEx^2 | Validation MRR | 0.8592 ± 0.0004 | #4 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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