Papers › RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Isaiah Onando Mulang', Saeedeh Shekarpour, Johannes Hoffart, Manohar Kaul
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).
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Extraction | NYT Corpus | RECON | P@10% | 87.5 | #2 of 7 | Archive leaderboard | report |
| Relation Extraction | NYT Corpus | RECON | P@30% | 74.1 | #2 of 7 | Archive leaderboard | report |
| Relationship Extraction (Distant Supervised) | New York Times Corpus | RECON | P@10% | 87.5 | #2 of 9 | Archive leaderboard | report |
| Relationship Extraction (Distant Supervised) | New York Times Corpus | RECON | P@30% | 74.1 | #2 of 9 | Archive leaderboard | report |
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