arXiv Machine Learning October 21, 2026 By Linyu Li, Zhi Jin, Yuanpeng He, Dongming Jin, Haoran Duan, Zhengwei Tao, Xuan Zhang, Jiandong Li, Huanyao Zhang, Gadeng Luosang, Nyima Tashi

Rethinking Generalization in Knowledge Graph Embedding: An Empirical Study and Sparse Peak Regularization

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

arXiv AI

Sep 4

Pattern Over-Generalization of Knowledge Graph Embedding

arXiv:2609. 03487v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space.

By Junsik Kim, Kangil Kim

rag benchmarks

arXiv AI

Jun 3

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.

By Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen

llms rag nlp efficiency benchmarks

arXiv Machine Learning

1d ago

When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels

The paper introduces Kernel-Anticipated Unlearning (KAU), a method for approximate unlearning of knowledge-graph embeddings that accounts for relearning during continued training. KAU uses the tangent kernel to model how forgotten facts’ scores change, expressing this response in closed form and guiding edits to minimize future leakage. Experiments show KAU reduces leakage by 46‑63% on FB15k-237 and 50‑82% on CoDEx-L compared to the strongest existing unlearner, while preserving higher utility.

By Parnian Fazel, Mohammad M Maheri, Hamed Haddadi

rag benchmarks

arXiv Machine Learning

Sep 10

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are...

By Xiaohua Lu, Liubov Tupikina, Mehwish Alam

benchmarks

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv Machine Learning

Jun 3

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin

rag benchmarks

One email a morning, machine-written

Related stories

Pattern Over-Generalization of Knowledge Graph Embedding

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

When Forgotten Facts Return: Relearning-Aware Unlearning of Knowledge Graph Embeddings via Tangent Kernels

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings