CtrlHGen: Controllable Hypothesis Generation for Knowledge Graph Abduction
A new framework named CtrlHGen has been introduced by researchers for generating controllable logical hypotheses in abductive reasoning using knowledge graphs. This type of reasoning produces plausible hypotheses based on observed entities, which is useful in fields like clinical diagnosis and scientific exploration. However, the absence of control often results in many irrelevant or redundant hypotheses, especially in extensive graphs. The controllable hypothesis generation task encounters two main obstacles: the collapse of the hypothesis space and excessive sensitivity of the hypotheses. To address these challenges, CtrlHGen employs a two-stage training process that combines supervised learning with reinforcement learning.
Key facts
- Abductive reasoning in knowledge graphs generates plausible logical hypotheses from observed entities.
- Applications include clinical diagnosis and scientific discovery.
- Lack of controllability yields numerous redundant or irrelevant hypotheses.
- CtrlHGen is a Controllable logical Hypothesis Generation framework.
- Two key challenges: hypothesis space collapse and hypothesis oversensitivity.
- Trained with supervised learning and reinforcement learning.
- Introduced to improve practical utility of abductive reasoning.
- Addresses limitations on large-scale knowledge graphs.
Entities
—