arXiv AI September 1, 2026 By Jiaqi Wang, Dongying Lin, Yang Yang, Yinan Liu, Bin Wang, Xiaochun Yang

LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information

The paper introduces CoSC, a method for knowledge graph completion that merges discrete structural coding with information from similar entities. An LLM first produces a candidate ranking based on structural codes, then refines this ranking using data from entities with comparable structures. Experiments on FB15k-237 demonstrate that CoSC achieves higher MRR and Hits@10 than existing baselines while staying competitive on Hits@1.

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llms

arXiv AI

Jul 21

RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion

arXiv:2604. 25693v2 Announce Type: replace Abstract: Most multi-modal knowledge graph completion (MMKGC) models use one embedding scorer to conduct both retrieval over the full entity set and final link prediction.

By Guanglin Niu, Bo Li

llms rag diffusion efficiency multimodal benchmarks

arXiv AI

Jun 16

Model Graph Inductive Learning for Knowledge Graph Completion

arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.

By Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan

rag benchmarks

Hugging Face Trending Papers

Jun 29

Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs

Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm.

rag benchmarks

arXiv AI

Jun 30

Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs

arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.

By Zihao Zheng, Borui Cai, Yao Zhao, Keshav Sood, Yong Xiang

rag benchmarks

arXiv Machine Learning

Jun 5

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.

By Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding, Di Jin, Guangquan Xu, Zhiyong Feng

llms benchmarks

arXiv AI

Jun 16

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

arXiv:2605. 27023v2 Announce Type: replace Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems.

By Yinan Liu, Wenjin Xu, Zhiyuan Zha, Xiaochun Yang, Bin Wang

rag nlp benchmarks

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RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion

Model Graph Inductive Learning for Knowledge Graph Completion

Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs

Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling