This thesis addresses the intersection of neural and symbolic artificial intelligence systems. Recent deep learning methods could memorize vast amount of world knowledge, but still have their limitation to conduct symbolic reasoning over them; while symbolic AI is good at solving reasoning tasks, but is inefficient for adapting to new knowledge. Prior efforts that bridge the two worlds mainly focus on building parsing-based systems, which require lots of annotated intermediate labels and hard to scale.

My ultimate research goal is to enable neural model to interact with symbolic reasoning module in a differentiable manner, and train such Neural-Symbolic model end-to-end without intermediate labels. To bring this vision about, I have conducted works on:

1. Designing Novel Reasoning Module: design differentiable neural modules that can conduct symbolic reasoning, including knowledge graph reasoning and complex Logical inference.2. Learning via Self-Supervision: train the neural model via self-supervision from structural and symbolic knowledge base without additional annotation. 3. Generalizing across Domains: the modular design of neural-symbolic system by its nature help to generalize better for Out-of-Distribution, Out-of-Vocabulary, cross-lingual and cross-type.

Putting these pieces together, I am pursuing the ultimate vision to build end-to-end Neural-Symbolic system that has the capacity of reasoning, advancing to true human intelligence.