Papers › Connecting Language and Knowledge with Heterogeneous Representations for Neural...
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
Peng Xu, Denilson Barbosa
Knowledge Bases (KBs) require constant up-dating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.
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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 | HRERE | P@10% | 84.9 | #3 of 7 | Archive leaderboard | report |
| Relation Extraction | NYT Corpus | HRERE | P@30% | 72.8 | #3 of 7 | Archive leaderboard | report |
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