Commonsense Knowledge Graph

Commonsense Knowledge Graphs (CSKGs) aim to represent and reason with the implicit, everyday knowledge humans possess but that is often absent from traditional knowledge bases. Current research focuses on improving CSKG construction, including methods for denoising automatically generated graphs, enriching them with contextual information and diverse knowledge sources (e.g., from large language models), and developing efficient algorithms for multi-hop reasoning over these graphs. These advancements are crucial for enhancing various natural language processing tasks, such as question answering, narrative generation, and argumentation, by providing models with access to a richer understanding of the world.