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).

本文提出了一种名为RECON的新颖方法，可以自动识别句子中的关系并将其与知识图谱对齐，使用图神经网络学习句子和知识图谱中存储的事实的表示，并评估了不同形式表示KG上下文对RECON性能的影响。经验评估结果显示，RECON对于NYT Freebase和Wikidata数据集表现均优于现有方法。

利用知识图谱上下文的关系抽取与图神经网络(RECON)