Graph reasoning over explicit semantic relation

Abstract

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research, particularly with the reliance on deep neural networks. These networks are integral to text reasoning processes, yet they present challenges in extracting and representing domain or commonsense knowledge, and they often lack robust logical reasoning capabilities. To address these issues, we introduce an innovative text reasoning framework. This framework is grounded in the use of a semantic relation graph and a graph neural network, designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning.

Our framework operates by extracting knowledge from a broad range of texts. It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process. Beginning with the core question, the framework methodically deduces key knowledge, using it as a guide to iteratively establish a complete evidence chain, thereby determining the final answer. Leveraging the advanced reasoning capabilities of the graph neural network, this approach is adept at multi-hop logical reasoning. It demonstrates strong performance in tasks like machine reading comprehension and question answering, while also clearly delineating the path of logical reasoning.