GPFL
Graph Path Feature Learning
**Graph 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.
Towards Learning Instantiated Logical Rules from Knowledge Graphs
https://paperswithcode.com/paper/efficient-rule-learning-with-template
Towards Learning Instantiated Logical Rules from Knowledge Graphs