KGFR introduces a Knowledge Graph Foundation Retriever that collaborates with large language models to enhance knowledge‑intensive question answering. By encoding relations with LLM‑generated descriptions and initializing entities from question roles, KGFR enables zero‑shot generalization to unseen knowledge graphs. Its Asymmetric Progressive Propagation technique efficiently handles large graphs, while a controllable reasoning loop allows the LLM to request candidate answers, supporting facts, and reasoning paths.
By Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu
arXiv:2609.39786v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based...
By Ola El Khatib, Djellel Difallah
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
Enrich-on-Graph (EoG) is a flexible framework that uses large language models to enrich knowledge graphs, thereby bridging the semantic gap between structured graphs and unstructured queries in complex reasoning tasks. By leveraging LLMs’ prior knowledge, EoG enables efficient evidence extraction from knowledge graphs, achieving precise and robust reasoning while maintaining low computational costs and scalability. The authors also introduce three graph quality evaluation metrics for query‑graph alignment, theoretically validate their optimization objectives, and demonstrate state‑of‑the‑art performance on two KGQA benchmark datasets.
By Songze Li, Zhiqiang Liu, Zhengke Gui, Huajun Chen, Wen Zhang
The paper introduces WFM, a Wiki Foundation Model designed to support complex agentic reasoning by combining dense document contexts with markdown-based topological linkages. It formalizes a Wiki Graph schema that preserves explicit topologies while embedding continuous semantics, and employs a query‑conditioned attentive aggregation for efficient message passing. The authors also propose an NCCL‑based protocol to reduce distributed system overhead, achieving a 10.5× training speedup and strong performance on long‑term memory and multi‑hop reasoning benchmarks.
By Junnan Dong, Linhao Luo, Senlei Zhang, Gong Chen, Taian Guo, Yifei Yu, Rong Tao, Tao Guo, Qian-Wen Zhang, Siyu An, Ruizhi Qiao, Xing Sun
RAW-Explainer is a new explainability method designed for path reasoning graph neural networks used in knowledge graph completion. It produces connected, concise, and interpretable subgraph explanations for link prediction by leveraging heterogeneous information and a random walk objective. The approach also addresses distribution shift when evaluating small explanatory subgraphs with a robust evaluator, achieving a balance between explanation quality and computational efficiency on standard benchmarks.
By Ryoji Kubo, Samyam Lamichhane, Djellel Difallah
arXiv:2605. 30747v2 Announce Type: replace Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns.
By Haoxiang Cheng, Yunfei Wang, Chao Chen, Kewei Cheng, Zhipeng Lin, Haoxuan Li, Changjun Fan, Shixuan Liu
MARS is a scalable knowledge‑graph question‑answering system that integrates large language models with knowledge graphs without requiring model fine‑tuning. It uses a structured retrieval process that links question entities to the graph, iteratively fetching next‑hop information and deciding when to stop and generate a SPARQL query. Evaluated on three KGQA benchmarks—including multilingual settings—MARS matches state‑of‑the‑art performance while remaining efficient and scalable.
By Nikit Srivastava, Daniel Vollmers, Ren\'e Speck, Nikolaos Karalis, Hamada M. Zahera, Axel-Cyrille Ngonga Ngomo
arXiv:2608.29263v1 Announce Type: new
Abstract: Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While inte...
By Yuwei Lou, Hao Hu, Yuzhou Jiang, Zongfei Zhang, Liang Wang, Jincai Liu, Jidong Ge, Xianping Tao
arXiv:2608.30556v1 Announce Type: new
Abstract: Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two di...
By Jun Hyeong Kim, Dongki Kim, Yinhua Piao, Sung Ju Hwang
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
arXiv:2609.14528v1 Announce Type: cross
Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
By Eduin E. Hernandez, Luis F. Garcia, Nurassyl Askar, Sergio A. Diaz, Stefano Rini