论文标题
知识增强的关系提取数据集
Knowledge-Enhanced Relation Extraction Dataset
论文作者
论文摘要
最近，利用辅助知识图的知识增强方法已出现在关系提取中，超过了传统的基于文本的方法。但是，据我们最大的知识，目前尚无公共数据集可用的公共数据集既包含证据句子和知识图，以提取知识增强的关系提取。为了解决这一差距，我们介绍了知识增强的关系提取数据集（KERED）。 Kered用关系事实注释每个句子，并通过实体链接为实体提供知识上下文。使用我们的策划数据集，我们比较了两个普遍的任务设置下的当代关系提取方法：句子级别和行李级。实验结果表明，KERED提供的知识图可以支持知识增强的关系提取方法。我们认为，Kered提供具有相应知识图的高质量关系提取数据集，以评估知识增强的关系提取方法的性能。我们的数据集可在以下网址提供：\ url {https://figshare.com/projects/kereds/kered/134459}
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches. However, to our best knowledge, there is currently no public dataset available that encompasses both evidence sentences and knowledge graphs for knowledge-enhanced relation extraction. To address this gap, we introduce the Knowledge-Enhanced Relation Extraction Dataset (KERED). KERED annotates each sentence with a relational fact, and it provides knowledge context for entities through entity linking. Using our curated dataset, We compared contemporary relation extraction methods under two prevalent task settings: sentence-level and bag-level. The experimental result shows the knowledge graphs provided by KERED can support knowledge-enhanced relation extraction methods. We believe that KERED offers high-quality relation extraction datasets with corresponding knowledge graphs for evaluating the performance of knowledge-enhanced relation extraction methods. Our dataset is available at: \url{https://figshare.com/projects/KERED/134459}
