Original: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Revised: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over .
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