arXiv AI

Subgraph-Aware Training of Language Models for Knowledge Graph Completion Using Structure-Aware Contrastive Learning

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

arXiv AI

Sep 3

PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion

PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.

By Yunchi Yang, Longlong Li, Cunquan Qu

llms rag benchmarks

arXiv Machine Learning

Sep 22

Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs

arXiv:2511.04473v3 Announce Type: replace Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various...

By Alberto Cattaneo, Carlo Luschi, Daniel Justus

llms nlp 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

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

Jul 21

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.

By Jiarui Feng, Donghong Cai, Yixin Chen, Muhan Zhang

llms fine-tuning safety

arXiv AI

Jun 19

Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

arXiv:2606. 19351v1 Announce Type: cross Abstract: Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support.

By Xinyan Zhu, Yaoqi Liu, Yue Gao, Huadong Ma, Cheng Yang, Chuan Shi

llms nlp benchmarks safety

arXiv AI

Aug 6

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

arXiv:2604. 12503v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios.

By Shuai Wang, Xixi Wang, Yinan Yu

llms rag nlp benchmarks safety

arXiv AI

Aug 7

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.

By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang

llms rag multimodal

arXiv Machine Learning

Oct 5

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...

By Dario Vajda

llms fine-tuning benchmarks

arXiv AI

Oct 5

KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning

arXiv:2605.14907v2 Announce Type: replace Abstract: Knowledge graph (KG) foundation models aim to generalize to graphs with unseen entities and relations by learning transferable relational structure...

By Yisen Gao, Jiaxin Bai, Haoyu Huang, Zhongwei Xie, Yufei Li, Hong Ting Tsang, Sirui Han, Yangqiu Song

llms fine-tuning

arXiv Machine Learning

Sep 3

GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping

The paper introduces GONE, a benchmark for evaluating knowledge unlearning in large language models using structured knowledge graphs, and presents Neighborhood-Expanded Distribution Shaping (NEDS), a framework that leverages graph connectivity to separate forgotten facts from their semantic neighborhood. GONE disentangles direct fact removal, reasoning-based leakage, and catastrophic forgetting, while NEDS achieves high unlearning efficacy and locality on LLaMA-3-8B and Mistral-7B. The dataset is publicly available on Hugging Face.

By Chahana Dahal, Ashutosh Balasubramaniam, Zuobin Xiong

llms fine-tuning efficiency benchmarks safety

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