  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).
