Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs (D19-1)
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| Challenge: | Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
| Approach: | They propose a Meta Relational Learning framework to do few-shot link prediction in KGs by observing only a few associative triples. |
| Outcome: | The proposed model achieves state-of-the-art results on few-shot link prediction KG benchmarks. |
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| Challenge: | Existing studies on knowledge graph completion require a large number of positive examples for each relation, but long-tail relations are more common in KGs and those newly added relations do not have many known triples for training. |
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Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs (2021.eacl-main)
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| Challenge: | Existing methods for few-shot link prediction are limited by having only a few examples of a relation . low-frequency relations are abundant in knowledge graphs, but link prediction for these relations is important . |
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| Challenge: | Existing methods to predict unseen triplets from knowledge graphs are limited by spurious information in KGs. |
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Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relational learning on knowledge graphs focus on leveraging specific relational information, but rich semantics inherent in KGs have been overlooked. |
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Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
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