To further investigate and leverage the transfer of logical structures across semantically related entities for improving generalization, several approaches can be considered. One way is to explore the use of transfer learning techniques to transfer knowledge learned from one domain to another. By training the model on a diverse range of entities with shared logical structures, the model can learn to generalize logical rules across different domains and apply them to unseen entities effectively.
Additionally, incorporating meta-learning strategies can help the model adapt to new logical structures and reasoning scenarios more efficiently. By exposing the model to a variety of logical constraints and scenarios during meta-training, it can learn to quickly adapt to new tasks and generalize logical structures across semantically related entities.
Furthermore, exploring techniques from neuro-symbolic learning can provide a framework for integrating symbolic reasoning with neural networks, allowing for a more structured and interpretable approach to logical reasoning. By combining symbolic reasoning with probabilistic reasoning in the LOCO-LMS framework, the model can leverage the strengths of both approaches to improve generalization and logical consistency across semantically related entities.