Triple Classification

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…

Knowledge Graph Embedding Triple Classification Knowledge Graphs Link Prediction

RADAR: Reasoning as Discrimination with Aligned Representations for LLM-based Knowledge Graph Reasoning

2026-02-25 · Bo Xue, Yuan Jin, Luoyi Fu, Jiaxin Ding 외 arxiv

Knowledge graph reasoning (KGR) infers missing facts, with recent advances increasingly harnessing the semantic priors and reasoning abilities of Large Language Models (LLMs). However, prevailing generative paradigms are…

Reinforcement Learning Triple Classification Relational Reasoning Link Prediction

SASA: Semantic-Aware Contrastive Learning Framework with Separated Attention for Triple Classification

2026-01-19 · Xu Xiaodan, Hu Xiaolin arxiv

Knowledge Graphs~(KGs) often suffer from unreliable knowledge, which restricts their utility. Triple Classification~(TC) aims to determine the validity of triples from KGs. Recently, text-based methods learn entity and r…

Representation Learning Binary Classification Triple Classification Contrastive Learning

LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings

2025-10-13 · Ting Li, Yang Yang, Yipeng Yu, Liang Yao 외 arxiv

Adversarial attacks on knowledge graph embeddings (KGE) aim to disrupt the model's ability of link prediction by removing or inserting triples. A recent black-box method has attempted to incorporate textual and structura…

Triple Classification Link Prediction

Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements

2025-10-13 · Rita T. Sousa, Heiko Paulheim arxiv

Knowledge graphs represent information as structured triples and serve as the backbone for a wide range of applications, including question answering, link prediction, and recommendation systems. A prominent line of rese…

Recommendation Systems Triple Classification Contrastive Learning Question Answering

Enhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models

2025-08-07 · Claudia d'Amato, Ivan Diliso, Nicola Fanizzi, Zafar Saeed arxiv

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs. Embedding models are trained relying on both positive and negative samples of trip…

Knowledge Graph Embedding Triple Classification Knowledge Graphs Link Prediction

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