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içgörü - Knowledge Graph Question Answering
Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs
Multi-relation questions can be answered via end-to-end single-step implicit reasoning using a novel Question-Aware Graph Convolutional Network (QAGCN) model.
CR-LT-KGQA: Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
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.
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
KGQA datasets need to support commonsense reasoning and long-tail knowledge for accurate answers.
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
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.
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