Rule Learners
Graph Path Feature Learning
Introduced by Gu et al. in Towards Learning Instantiated Logical Rules from Knowledge GraphsGraph Path Feature Learning is a probabilistic rule learner optimized to mine instantiated first-order logic rules from knowledge graphs. Instantiated rules contain constants extracted from KGs. Compared to abstract rules that contain no constants, instantiated rules are capable of explaining and expressing concepts in more detail. GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules until a certain degree of template saturation is achieved, then specializes the generated templates into instantiated rules.
Source: Towards Learning Instantiated Logical Rules from Knowledge Graphs
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Federated Learning | 2 | 28.57% |
| Fairness | 1 | 14.29% |
| Personalized Federated Learning | 1 | 14.29% |
| Inductive knowledge graph completion | 1 | 14.29% |
| Knowledge Graph Completion | 1 | 14.29% |
| Knowledge Graphs | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
