mlsanityData to 14 Oct 2026

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

arXiv 2112.09340 ↗First posted 17 Dec 2021cs.LG cs.CLcited by 10

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Abstract (arXiv)

Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a powerful classifier for missing link prediction. We conduct experiments on multiple benchmark datasets, and demonstrate that KGBoost outperforms state-of-the-art methods across most datasets. Furthermore, as compared with models trained by end-to-end optimization, KGBoost works well under the low-dimensional setting so as to allow a smaller model size.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 17 Dec 2021.

Builds on

14 of 29 references.

References · 14

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  3. 2016Complex Embeddings for Simple Link Predictioncited by 902
  4. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  5. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  6. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  7. 2019Quaternion Knowledge Graph Embeddingscited by 175
  8. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  9. 2017KBGAN: Adversarial Learning for Knowledge Graph Embeddingscited by 62
  10. 2013A Semantic Matching Energy Function for Learning with Multi-relational Datacited by 61
  11. 2019InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactionscited by 58
  12. 2019Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embeddingcited by 38
2 more
  1. 2015Type-Constrained Representation Learning in Knowledge Graphscited by 34
  2. 2018Incorporating GAN for Negative Sampling in Knowledge Representation Learningcited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 3; 2025: 0; 2026: 1.

10 papers.

  1. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  2. 2023An Overview on Language Models: Recent Developments and Outlookcited by 22
  3. 2024Time Sensitive Knowledge Editing through Efficient Finetuningcited by 14
  4. 2021CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embeddingcited by 10
  5. 2022CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operationscited by 10
  6. 2022GreenKGC: A Lightweight Knowledge Graph Completion Methodcited by 9
  7. 2024Negative Sampling in Knowledge Graph Representation Learning: A Reviewcited by 9
  8. 2023Knowledge Graph Embedding with 3D Compound Geometric Transformationscited by 3
  9. 2024Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Modelscited by 0
  10. 2026Plausibility-Driven Prioritization of Candidate Biomedical Annotationscited by 0