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A novel framework called FAIR GraphRAG integrates FAIR Digital Objects with graph-based retrieval to enhance retrieval-augmented generation for semantic data analysis, improving question answering accuracy and adherence to FAIR principles, demonstrated on a biomedical dataset.
This comprehensive survey systematically reviews graph neural network-based methods across the entire knowledge graph pipeline, proposing a novel two-level taxonomy and discussing challenges and future research directions.
Proposes AgentKGV, an agentic LLM-RAG framework with two-stage training (distillation SFT and trajectory-level GRPO) for verifying facts in knowledge graphs, achieving significant improvements on the T-REx benchmark while reducing retrieval calls.
This paper proposes a complementarity-theoretic interpretation for integrating knowledge graphs and explainable AI to support defensible decisions in urban mining pre-demolition assessment. It defines four KG-XAI integration modes (Lifting, Constraining, Typing, and Revising) and illustrates them with a fire-door example.
InductWave proposes a wavelet-based inductive embedding method for multi-hop logical query answering on knowledge graphs, achieving competitive performance with fewer message-passing layers.
This paper proposes a novel data-driven pipeline using LLMs to extract semantic triples from unstructured text and construct knowledge graphs for monitoring technological convergence. The method is validated on arXiv preprints and USPTO patent applications, demonstrating scalability for technology forecasting.
This paper presents a use case from the European project LLMs4EU and ALT-EDIC infrastructure, focusing on adapting foundation models to Social Sciences and Humanities (SSH) research practices by integrating knowledge graphs and multilingual scholarly corpora. The approach aims to support tasks like question answering and literature review while ensuring domain sensitivity and regulatory compliance.
This paper introduces the Narrative World Model (NWM), a memory system for long-form fiction writers that uses narratology-grounded typed temporal-state graphs and query-conditioned hybrid retrieval to answer multi-hop questions about evolving story state. The system significantly outperforms existing temporal-knowledge-graph frameworks like Graphiti on benchmark narratological QA tasks.
A developer reflects on when GraphRAG is preferable to standard RAG for AI agents, noting it becomes valuable for multi-hop reasoning and relationship understanding, but adds significant complexity.
A talk from WeAreDevelopers World Congress 2026 on modeling organizations as graphs to reveal hidden networks where work actually happens, alongside related videos on graph databases, knowledge graphs, and developer conferences.
A CIO shares observations on the challenges of applying semantic layers and ontologies to enterprise AI agents, highlighting the need for dynamic context selection, continuous evolution of institutional knowledge, and representing judgment rather than just static facts.
LeanRAG is an open-source RAG framework that uses knowledge graphs, semantic aggregation, and hierarchical retrieval to reduce redundancy in retrieval pipelines, providing grounded answers with concise evidence paths.
This paper frames regulatory document review as an LLM-guided planning problem, using a vectorless document tree with browse, read, and search tools and a dynamic knowledge graph as state. On a 200-question benchmark over NuScale FSAR documents, the system achieves 81.5% accuracy with 0.93 RAGAS Faithfulness, significantly outperforming existing RAG methods.
This paper proposes a modular pipeline that uses a domain-specific knowledge graph to generate multi-hop QA pairs and fine-tune a reasoning LLM (Qwen3-4B) for the travel domain, achieving 82.4% exact match accuracy, significantly outperforming the baseline.
Introduces MKG-RAG-Bench, a cross-domain benchmark for evaluating retrieval in multimodal knowledge graph-augmented generation, demonstrating that effective multimodal retrieval remains challenging and critical for downstream generation quality.
This paper from SJTU and Tsinghua systematically evaluates 12 agent memory systems from a data management perspective, decomposing memory into four modules and providing guidelines on when to use RAG, vector databases, or knowledge graphs for long-term agent memory.
Hyper-Extract is a CLI tool that transforms messy, unstructured documents into structured knowledge such as knowledge graphs, hypergraphs, temporal/spatial graphs, and Obsidian vaults, supporting local LLM inference and MCP integration.
Hyper-Extract is an open-source framework that converts messy documents into typed knowledge structures, supporting multiple graph architectures like GraphRAG, LightRAG, and KG-Gen, with 10+ extraction engines and 80+ YAML templates for various domains.
AutoFlow discusses the critical challenge of trust in AI, proposing external verification methods such as knowledge graphs and mathematical consistency checks, and announces acceptance into the NVIDIA Inception Program to advance research into trustworthy AI systems.
This paper formalizes the 'Impedance Mismatch' between foundation models and knowledge graphs, and proposes a theoretical roadmap for neuro-symbolic fusion using structured residual streams, vector symbolic architectures, and orthogonal subspace editing.