mlsanityData to 14 Oct 2026

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

arXiv 2205.04692 ↗First posted 10 May 2022cs.CL cs.AIcited by 15

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Abstract (arXiv)

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 10 May 2022.

Builds on

21 of 31 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2016Matching Networks for One Shot Learningcited by 2,255
  6. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  7. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  8. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  9. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 396
  10. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  11. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 266
  12. 2019Inductive Relation Prediction by Subgraph Reasoningcited by 239
9 more
  1. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  2. 2017Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approachcited by 102
  3. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  4. 2018One-Shot Relational Learning for Knowledge Graphscited by 78
  5. 2019Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphscited by 73
  6. 2018Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embeddingcited by 53
  7. 2021Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphscited by 48
  8. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  9. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45

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Citing papers per year: 2022: 1; 2023: 9; 2024: 1; 2025: 4.

15 papers.

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  1. 2022Relational Message Passing for Fully Inductive Knowledge Graph Completioncited by 30
  2. 2023Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Typescited by 21
  3. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  4. 2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphscited by 14
  5. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 10
  6. 2023Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embeddingcited by 8
  7. 2023Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graphcited by 7
  8. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  9. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  10. 2024Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphscited by 4
  11. 2023Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphscited by 2
  12. 2023NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learningcited by 1
3 more
  1. 2025Beyond Completion: A Foundation Model for General Knowledge Graph Reasoningcited by 1
  2. 2025FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Datacited by 1
  3. 2025DynaGen: Unifying Temporal Knowledge Graph Reasoning with Dynamic Subgraphs and Generative Regularizationcited by 0