论文标题
侦察：使用图形神经网络中知识图上下文的关系提取
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
论文作者
论文摘要
在本文中，我们提出了一种名为Recon的新方法，该方法将自动识别句子中的关系（句子关系提取），并与知识图（KG）保持一致。侦察使用图形神经网络来学习句子的表示形式以及存储在kg中的事实，从而提高了整体提取质量。这些事实，包括实体属性（标签，别名，描述，实例）和事实三元组，尚未在最先进的方法的状态中共同使用。我们评估表示KG环境对侦察表现的各种形式的影响。对两个标准关系提取数据集的经验评估表明，在NYT FreeBase和Wikidata数据集上，侦察的表现明显优于所有最先进的方法。侦察报告87.23 F1得分（vs 82.29基线）在Wikidata数据集上，而在NYT Freebase上，与以前的81.3（P@10）和63.1（P@30）相比，报告的值为87.5（P@10）和74.1（P@30）。
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). RECON uses a graph neural network to learn representations of both the sentence as well as facts stored in a KG, improving the overall extraction quality. These facts, including entity attributes (label, alias, description, instance-of) and factual triples, have not been collectively used in the state of the art methods. We evaluate the effect of various forms of representing the KG context on the performance of RECON. The empirical evaluation on two standard relation extraction datasets shows that RECON significantly outperforms all state of the art methods on NYT Freebase and Wikidata datasets. RECON reports 87.23 F1 score (Vs 82.29 baseline) on Wikidata dataset whereas on NYT Freebase, reported values are 87.5(P@10) and 74.1(P@30) compared to the previous baseline scores of 81.3(P@10) and 63.1(P@30).
