mlsanityData to 14 Oct 2026

LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding

arXiv 2102.10826 ↗First posted 22 Feb 2021cs.AI cs.CL cs.LGcited by 11

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Abstract (arXiv)

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models cannot make a proper trade-off between the graph context and the model complexity, which makes them still far from satisfactory. In this paper, we propose a lightweight framework named LightCAKE for context-aware KGE. LightCAKE explicitly models the graph context without introducing redundant trainable parameters, and uses an iterative aggregation strategy to integrate the context information into the entity/relation embeddings. As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 22 Feb 2021.

Builds on

12 of 22 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Graph Attention Networkscited by 8,923
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  5. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  6. 2016Complex Embeddings for Simple Link Predictioncited by 902
  7. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  8. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  9. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 266
  10. 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
  11. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 175
  12. 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 138

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 5; 2026: 1.

11 papers.

  1. 2024Deep Frequency Derivative Learning for Non-stationary Time Series Forecastingcited by 13
  2. 2023Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoningcited by 9
  3. 2023Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoningcited by 9
  4. 2024FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimizationcited by 9
  5. 2025Deep Cut-informed Graph Embedding and Clusteringcited by 7
  6. 2025scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Datacited by 6
  7. 2025Rethinking Graph Contrastive Learning through Relative Similarity Preservationcited by 5
  8. 2025Soft Graph Clustering for single-cell RNA Sequencing Datacited by 2
  9. 2025Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linkingcited by 1
  10. 2022Context-driven Visual Object Recognition based on Knowledge Graphscited by 0
  11. 2026Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matchingcited by 0