Multi-relation questions can be answered via end-to-end single-step implicit reasoning using a novel Question-Aware Graph Convolutional Network (QAGCN) model.
Knowledge graph question answering datasets need to evolve to include commonsense reasoning and focus on long-tail entities to challenge the limitations of existing methods.
KGQA datasets need to support commonsense reasoning and long-tail knowledge for accurate answers.
The author introduces the CR-LT-KGQA dataset to address the limitations of existing KGQA datasets by focusing on commonsense reasoning and long-tail entities, challenging LLMs prone to hallucination.