Papers with Hits@10
RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion (2026.eacl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. Large Language Models (LLMs) have sparked interest in using pretrained generative models for TKG completion. |
| Approach: | They propose a framework that allows for rule-based multi-hop sampling and contrastive fine-tuning to shape relational compatibility. |
| Outcome: | Experiments show that RECIPE-TKG outperforms prior LLM-based methods across input regimes. |
Adaptive Convolution for Multi-Relational Learning (N19-1)
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| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts (2022.coling-1)
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| Challenge: | Existing methods for knowledge Graph Completion (KGC) fail in unseen relation representations. |
| Approach: | They propose to use three kinds of graphs to obtain unseen relation representations . they propose to decouple mixture-of-graph experts (DMoG) for unseened relations learning . |
| Outcome: | The proposed method outperforms the state-of-the-art methods on unseen relation representations. |