Rule-based systems
Symbolic rule learning
Introduced by Ott et al. in Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphsSymbolic rule learning methods find regularities in data that can be expressed in the form of 'if-then' rules based on symbolic representations of the data.
Source: Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphsPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Knowledge Graphs | 2 | 22.22% |
| Link Prediction | 2 | 22.22% |
| Image Classification | 1 | 11.11% |
| Clustering | 1 | 11.11% |
| Subgroup Discovery | 1 | 11.11% |
| Inductive knowledge graph completion | 1 | 11.11% |
| Inductive Link Prediction | 1 | 11.11% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
