An open-source Python framework for real-time, graph-based knowledge in Neo4j

How LLMs can used in a knowledge extraction and visualization pipeline

We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.

Choosing the Right Format: How Knowledge Graph Layouts Impact AI Reasoning ... 👉 Why This Matters Most AI systems blend knowledge graphs (structured data) with large language models (flexible reasoning). But there’s a hidden variable: "how" you translate the graph into text for the AI. Researchers discovered that the formatting choice alone can swing performance by up to "17.5%" on reasoning tasks. Imagine solving 1 in 5 more problems correctly just by adjusting how you present data. 👉 What They Built KG-LLM-Bench is a new benchmark to test how language models reason with knowledge graphs. It includes five tasks: - Triple verification (“Does this fact exist?”) - Shortest path finding (“How are two concepts connected?”) - Aggregation (“How many entities meet X condition?”) - Multi-hop reasoning (“Which entities linked to A also have property B?”) - Global analysis (“Which node is most central?”) The team tested seven models (Claude, GPT-4o, Gemini, Llama, Nova) with five ways to “textualize” graphs, from simple edge lists to structured JSON and semantic web formats like RDF Turtle. 👉 Key Insights 1. Format matters more than assumed:   - Structured JSON and edge lists performed best overall, but results varied by task.   - For example, JSON excels at aggregation tasks (data is grouped by entity), while edge lists help identify central nodes (repeated mentions highlight connections). 2. Models don’t cheat: Replacing real entity names with fake ones (e.g., “France” → “Verdania”) caused only a 0.2% performance drop, proving models rely on context, not memorized knowledge. 3. Token efficiency:   - Edge lists used ~2,600 tokens vs. JSON-LD’s ~13,500. Shorter formats free up context space for complex reasoning.   - But concise ≠ always better: structured formats improved accuracy for tasks requiring grouped data. 4. Models struggle with directionality:   Counting outgoing edges (e.g., “Which countries does France border?”) is easier than incoming ones (“Which countries border France?”), likely due to formatting biases. 👉 Practical Takeaways - Optimize for your task: Use JSON for aggregation, edge lists for centrality. - Test your model: The best format depends on the LLM—Claude thrived with RDF Turtle, while Gemini preferred edge lists. - Don’t fear pseudonyms: Masking real names minimally impacts performance, useful for sensitive data. The benchmark is openly available, inviting researchers to add new tasks, graphs, and models. As AI handles larger knowledge bases, choosing the right “data language” becomes as critical as the reasoning logic itself. Paper: [KG-LLM-Bench: A Scalable Benchmark for Evaluating LLM Reasoning on Textualized Knowledge Graphs] Authors: Elan Markowitz, Krupa Galiya, Greg Ver Steeg, Aram Galstyan

This blog post is part of a series that dives into various aspects of SAP’s approach to Generative AI, and its technical underpinnings. In previous blog posts of this series, you learned about how to use large language models (LLMs) for developing AI applications in a trustworthy and reliable manner...

Discover how to empower LLM development through effective knowledge graph visualization. Learn to leverage yFiles for intuitive, interactive diagrams that simplify debugging and optimization in AI applications.

Multi-Layer Agentic Reasoning: Connecting Complex Data and Dynamic Insights in Graph-Based RAG Systems 🛜 At the most fundamental level, all approaches rely… | 11 comments on LinkedIn

Build a graph for RAG application for a price of a chocolate bar! What is GraphRAG for you? What is GraphRAG? What does GraphRAG mean from your perspective? What if you could have a standard RAG and a GraphRAG as a combi-package, with just a query switch? The fact is, there is no concrete, universal

To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren't a good fit to train large language models.

🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage. Achieving that by Semantic-Aware Heterogeneous Graph…

I love Markus J. Buehler's work, and his latest paper "Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks" does not disappoint, revealing… | 19 comments on LinkedIn

🏆🚣MiniRAG Introduces Near-LLM Accurate RAG for Small Language Models with Just 25% of the Storage. Achieving that by Semantic-Aware Heterogeneous Graph…

Dynamic Reasoning Graphs + LLMs = 🤝 Large Language Models (LLMs) often stumble on complex tasks when confined to linear reasoning. What if they could dynamically restructure their thought process like humans? A new paper introduces Adaptive Graph of Thoughts (AGoT), a test-time framework that replaces rigid prompting strategies (like Chain/Tree of Thought) with dynamic directed acyclic graphs (DAGs). Instead of forcing fixed reasoning steps, AGoT recursively decomposes problems into sub-tasks, selectively expanding only the most critical pathways. This is crucial for industries like scientific research or legal analysis, where problems demand non-linear, nested reasoning. The key innovation lies in complexity checks: AGoT assesses each reasoning node, spawning sub-graphs for intricate subtasks while resolving simpler ones directly. This mirrors how experts allocate mental effort—drilling into uncertainties while streamlining obvious steps. The framework achieved 46.2% improvement on GPQA (a notoriously hard science QA benchmark), rivaling gains from compute-heavy fine-tuning. By unifying chain, tree, and graph paradigms, AGoT retains CoT’s clarity, ToT’s exploration, and GoT’s flexibility without manual tuning. The result? LLMs that self-adapt their reasoning depth based on problem complexity—no architectural changes needed. For AI practitioners, AGoT’s DAG structure offers a principled interface to scale reasoning modularly. ↓ 𝐖𝐚𝐧𝐧𝐚 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐦𝐢𝐬𝐬𝐞𝐝? Join my newsletter with 50k+ readers that breaks down all you need to know about the latest LLM research: llmwatch.com 💡

Dynamic Reasoning Graphs + LLMs = 🤝 Large Language Models (LLMs) often stumble on complex tasks when confined to linear reasoning. What if they could… | 10 comments on LinkedIn

Today, I conducted a comparison between ChatGPT and DeepSeek chat capabilities by providing them with a schema and a natural language question. I tasked them…

David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.

This study, sponsored by Graphwise, explores LLMs and RAG in enterprises and provides statistics from executives across various industries.

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.

🧱Building Knowledge Graphs with LLM Graph Transformer A deep dive into LangChain’s implementation of graph construction with LLMs If you want to try out… | 32 comments on LinkedIn

Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.

The Power of Graph-Native Intelligence for Agentic AI Systems How Entity Resolution, Knowledge Fusion, and Extension Frameworks Transform Enterprise AI ⚡…

We are launching a series of benchmarks to prove the value of GraphRAG for question & answering on complex documents. The process is simple, we ingest the…

A GCP native, fully serverless implementation that you will replicate in minutes

In this video, I introduce LightRAG, a new, cost-effective retrieval augmented generation (RAG) method that combines knowledge graphs and embedding-based ret...

Data Structures for Hyper-Personalized Lesson Plans

Document Metadata Generation, File Directory & Two-Step Information Retrieval Processes

Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.

Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that… | 19 comments on LinkedIn

It was recorded and I hope it'll get… — Emil Eifrem (@emileifrem)