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 .
<meaning-changed> 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 .
Recently, knowledge graph (KG) augmented 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 .