RECON: relation extraction using knowledge graph context in a graph neural network
Proceedings of the Web Conference 2021, 2021•dl.acm.org
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 …
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 …
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.
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