| 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. |
| Approach: | They propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddments and one-hop graph structures. |
| Outcome: | The proposed framework improves on existing embedding models and eliminates the need for retraining when dealing with newly added relations. |
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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. |
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Challenging the Assumption of Structure-based embeddings in Few- and Zero-shot Knowledge Graph Completion (2022.lrec-1)
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| Challenge: | Existing work on Knowledge Graph completion only uses textual descriptive data . knowledge graphs are incomplete because not every relation has been observed at the time of their construction. |
| Approach: | They propose to use textual descriptive data to enrich benchmark data sets for Few- and Zero-shot Knowledge Graph completion tasks. |
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Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation (2024.naacl-long)
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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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zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations . |
| Approach: | They propose to embed knowledge graphs (TKGF) entities and relations based on observed contexts into embedding-based methods to model unseen zero-shot relations. |
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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). |
| Approach: | They propose to capture both entity and relation features in any given neighborhood and encapsulate relation clusters and multi-hop relations in their attention-based model. |
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Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases (2020.findings-emnlp)
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| Challenge: | Existing methods for multi-hop relation reasoning require limited data for each query relation, resulting in limited interpretation. |
| Approach: | They propose a few-shot multi-hop relation learning model that uses reinforcement learning to model sequential steps of multi-hopping reasoning and performs heterogeneous structure encoding and knowledge-aware search space pruning. |
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Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations (D19-1)
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| Challenge: | Existing methods for multi-hop reasoning assume that every relation has enough triples for training . however, performance drops significantly on few-shot relations . |
| Approach: | They propose a meta-based multi-hop reasoning method that learns meta parameters from high-frequency relations that could quickly adapt to few-shot scenarios. |
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Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)
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| Challenge: | Existing models for KG-to-text generation are based on pretrained language models. |
| Approach: | They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. |
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Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)
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| Challenge: | Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models. |
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Knowledge Graph Alignment with Entity-Pair Embedding (2020.emnlp-main)
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| Challenge: | Existing methods for Knowledge Graph (KG) alignment are not satisfactory. |
| Approach: | They propose a method that directly learns embeddings of entity-pairs for KG alignment. |
| Outcome: | The proposed approach can achieve state-of-the-art on five real-world datasets. |