# Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2104.13095 · HTML: https://mlsanity.com/paper/2104.13095/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Low-Dimensional Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2005.00545/) (2020)
- [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017)
- [Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/1906.01195/) (2019)
- [One-Shot Relational Learning for Knowledge Graphs](https://mlsanity.com/paper/1808.09040/) (2018)
- [Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1909.01515/) (2019)
- [Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/1911.11298/) (2019)
- [A Semantic Matching Energy Function for Learning with Multi-relational Data](https://mlsanity.com/paper/1301.3485/) (2013)
- [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://mlsanity.com/paper/1704.07130/) (2017)
- [Adaptive Attentional Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2010.09638/) (2020)
- [Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations](https://mlsanity.com/paper/1908.11513/) (2019)
- [Cognitive Knowledge Graph Reasoning for One-shot Relational Learning](https://mlsanity.com/paper/1906.05489/) (2019)

## Built on by (most cited first, 30 of 32)

- [Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14170/) (2021)
- [Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion](https://mlsanity.com/paper/2209.07299/) (2022)
- [Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.08654/) (2022)
- [Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2304.08183/) (2023)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023)
- [MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning](https://mlsanity.com/paper/2209.01416/) (2022)
- [Few-Shot Learning on Graphs](https://mlsanity.com/paper/2203.09308/) (2022)
- [On the Evolution of Knowledge Graphs: A Survey and Perspective](https://mlsanity.com/paper/2310.04835/) (2023)
- [Message Intercommunication for Inductive Relation Reasoning](https://mlsanity.com/paper/2305.14074/) (2023)
- [Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction](https://mlsanity.com/paper/2205.10621/) (2022)
- [Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2209.01205/) (2022)
- [A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge](https://mlsanity.com/paper/2301.01172/) (2023)
- [A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning](https://mlsanity.com/paper/2402.01440/) (2024)
- [Towards Few-shot Self-explaining Graph Neural Networks](https://mlsanity.com/paper/2408.07340/) (2024)
- [Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs](https://mlsanity.com/paper/2410.09123/) (2024)
- [TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion](https://mlsanity.com/paper/2504.03720/) (2025)
- [MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2302.00893/) (2023)
- [Few-shot Link Prediction on N-ary Facts](https://mlsanity.com/paper/2305.06104/) (2023)
- [Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2306.09519/) (2023)
- [Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2403.04521/) (2024)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion](https://mlsanity.com/paper/2203.11639/) (2022)
- [Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing](https://mlsanity.com/paper/2404.00589/) (2024)
- [Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts](https://mlsanity.com/paper/2501.15688/) (2025)
- [MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning](https://mlsanity.com/paper/2510.23013/) (2025)
- [A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion](https://mlsanity.com/paper/2410.04488/) (2024)
- [Meta-Semantics Augmented Few-Shot Relational Learning](https://mlsanity.com/paper/2505.05684/) (2025)
- [ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2505.07171/) (2025)
- [FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs](https://mlsanity.com/paper/2510.00894/) (2025)

## Abstract (arXiv)

Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to enhance its semantic representation. However, noise neighbor information might be amplified when the neighborhood is excessively sparse and no neighbor is available to represent the few-shot relation. Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances. Thus, inferring complex relations in the few-shot scenario is difficult for FKGC models due to limited training instances. In this paper, we propose a few-shot relational learning with global-local framework to address the above issues. At the global stage, a novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods. For the local stage, a meta-learning based TransH (MTransH) method is designed to model complex relations and train our model in a few-shot learning fashion. Extensive experiments show that our model outperforms the state-of-the-art FKGC approaches on the frequently-used benchmark datasets NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on Wiki-One by the metric Hits@10.