Symbolic Inference

Symbolic inference focuses on using logical rules and formal reasoning to derive conclusions from data, aiming to improve the explainability and reliability of AI systems. Current research emphasizes integrating symbolic methods with neural networks, particularly for knowledge graph completion and automated theorem proving, exploring how to augment existing rule sets and leverage pre-trained language models' encoded linguistic knowledge for enhanced inference capabilities. This work is significant because it addresses limitations of purely neural approaches by incorporating explicit reasoning, leading to more robust and interpretable AI systems with applications in diverse fields like knowledge representation and question answering.