# TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2504.03720 · HTML: https://mlsanity.com/paper/2504.03720/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 18 of 18)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [Knowledge Graph Prompting for Multi-Document Question Answering](https://mlsanity.com/paper/2308.11730/) (2023)
- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Embedding Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/1806.01445/) (2018)
- [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)
- [FactKG: Fact Verification via Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2305.06590/) (2023)
- [Adaptive Attentional Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2010.09638/) (2020)
- [Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks](https://mlsanity.com/paper/1607.01426/) (2016)
- [Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2104.13095/) (2021)
- [Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information](https://mlsanity.com/paper/2211.08169/) (2022)

## Built on by (most cited first, 3 of 3)

- [Multi-hop Reasoning and Retrieval in Embedding Space: Leveraging Large Language Models with Knowledge](https://mlsanity.com/paper/2603.13266/) (2026)
- [Neural-Symbolic Logic Query Answering in Non-Euclidean Space](https://mlsanity.com/paper/2603.15633/) (2026)
- [Prompt-tuning with Attribute Guidance for Low-resource Entity Matching](https://mlsanity.com/paper/2603.19321/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performance on downstream tasks. Additionally, the relationships in real-world knowledge graphs often follow a long-tail distribution, meaning that most relations are represented by only a few training triplets. To address these challenges, few-shot learning has been introduced. Few-shot KG completion aims to make accurate predictions for triplets involving novel relations when only a limited number of training triplets are available. Although many methods have been proposed, they typically learn each relation individually, overlooking the correlations between different tasks and the relevant information in previously trained tasks. In this paper, we propose a transfer learning-based few-shot KG completion method (TransNet). By learning the relationships between different tasks, TransNet effectively transfers knowledge from similar tasks to improve the current task's performance. Furthermore, by employing meta-learning, TransNet can generalize effectively to new, unseen relations. Extensive experiments on benchmark datasets demonstrate the superiority of TransNet over state-of-the-art methods. Code can be found at https://github.com/lihuiliullh/TransNet/tree/main