Last week I was fortunate to attend the Knowledge Graph Conference in NYC! Here are a few trends that span multiple presentations and conversations. - AI and LLM Integration: A major focus [again this year] was how LLMs can be used to enrich knowledge graphs and how knowledge graphs, in turn, can improve LLM outputs. This included using LLMs for entity extraction, verification, inference, and query generation. Many presentations demonstrated how grounding LLMs in knowledge graphs leads to more accurate, contextual, and explainable AI responses. - Semantic Layers and Enterprise Knowledge: There was a strong emphasis on building semantic layers that act as gateways to structured, connected enterprise data. These layers facilitate data integration, governance, and more intelligent AI agents. Decentralized semantic data products (DPROD) were discussed as a framework for internal enterprise data ecosystems. - From Data to Knowledge: Many speakers highlighted that AI is just the “tip of the iceberg” and the true power lies in the data beneath. Converting raw data into structured, connected knowledge was seen as crucial. The hidden costs of ignoring semantics were also discussed, emphasizing the need for consistent data preparation, cleansing, and governance. - Ontology Management and Change: Managing changes and governance in ontologies was a recurring theme. Strategies such as modularization, version control, and semantic testing were recommended. The concept of “SemOps” (Semantic Operations) was discussed, paralleling DevOps for software development. - Practical Tools and Demos: The conference included numerous demos of tools and platforms for building, querying, and visualizing knowledge graphs. These ranged from embedded databases like KuzuDB and RDFox to conversational AI interfaces for KGs, such as those from Metaphacts and Stardog. I especially enjoyed catching up with the Semantic Arts team (Mark Wallace, Dave McComb and Steve Case), talking Gist Ontology and SemOps. I also appreciated the detailed Neptune Q&A I had with Brian O'Keefe, the vision of Ora Lassila and then a chance meeting Adrian Gschwend for the first time, where we connected on LinkML and Elmo as a means to help with bidirectional dataflows. I was so excited by these conversations that I planned to have two team members join me in June at the Data Centric Architecture Workshop Forum, https://www.dcaforum.com/

In a previous post I was touching on how ontologies are foundational to many data activities, yet "obscure". As a consequence, the different roles of ontologies are not always known among people that make use of them, as they may focus only on some of the aspects relevant for specific use cases.

🤺 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀 𝗖𝗹𝗲𝗮𝗿: 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗶𝗹𝗹 𝗡𝗘𝗘𝗗 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 Why? It combines Multi-hop reasoning, Non-Parameterized / Learning-Based Retrieval, Topology-Aware Prompting. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗚𝗿𝗮𝗽𝗵-𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚)? ✩ LLMs hallucinate. ✩ LLMs forget. ✩ LLMs struggle with complex reasoning. Graphs connect facts. They organize knowledge into neat, structured webs. So when RAG retrieves from a graph, the LLM doesn't just guess — it reasons. It follows the map. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗧𝗵𝗲 𝟰-𝗦𝘁𝗲𝗽 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗼𝗳 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 1️⃣ — User Query: The user asks a question. ("Tell me how Einstein used Riemannian geometry?") 2️⃣ — Retrieval Module: The system fetches the most structurally relevant knowledge from a graph. (Entities: Einstein, Grossmann, Riemannian Geometry.) 3️⃣ — Prompting Module: Retrieved knowledge is reshaped into a golden prompt — sometimes as structured triples, sometimes as smart text. 4️⃣ — Output Response: LLM generates a fact-rich, logically sound answer. ﹌﹌﹌﹌﹌﹌﹌﹌﹌ 🤺 𝗦𝘁𝗲𝗽 𝟭: 𝗕𝘂𝗶𝗹𝗱 𝗚𝗿𝗮𝗽𝗵-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 ✩ Use Existing Knowledge Graphs like Freebase or Wikidata — structured, reliable, but static. ✩ Or Build New Graphs From Text (OpenIE, instruction-tuned LLMs) — dynamic, adaptable, messy but powerful. 🤺 𝗦𝘁𝗲𝗽 𝟮: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 ✩ Non-Parameterized Retrieval (Deterministic, Probabilistic, Heuristic) ★ Think Dijkstra's algorithm, PageRank, 1-hop neighbors. Fast but rigid. ✩ Learning-Based Retrieval (GNNs, Attention Models) ★ Think "graph convolution" or "graph attention." Smarter, deeper, but heavier. ✩ Prompting Approaches: ★ Topology-Aware: Preserve graph structure — multi-hop reasoning. ★ Text Prompting: Flatten into readable sentences — easier for vanilla LLMs. 🤺 𝗦𝘁𝗲𝗽 𝟯: 𝗚𝗿𝗮𝗽𝗵-𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 ✩ Sequential Pipelines: Straightforward query ➔ retrieve ➔ prompt ➔ answer. ✩ Loop Pipelines: Iterative refinement until the best evidence is found. ✩ Tree Pipelines: Parallel exploration ➔ multiple knowledge paths at once. 🤺 𝗦𝘁𝗲𝗽 𝟰: 𝗚𝗿𝗮𝗽𝗵-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗧𝗮𝘀𝗸𝘀 ✩ Knowledge Graph QA (KGQA): Answering deep, logical questions with graphs. ✩ Graph Tasks: Node classification, link prediction, graph summarization. ✩ Domain-Specific Applications: Biomedicine, law, scientific discovery, finance. ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ Join my 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴. Skip the fluff and build real AI agents — fast. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁: ✅ Create Smart Agents + Powerful RAG Pipelines ✅ Master 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗖𝗿𝗲𝘄𝗔𝗜 & 𝗦𝘄𝗮𝗿𝗺 – all in one training ✅ Projects with Text, Audio, Video & Tabular Data 𝟰𝟲𝟬+ engineers already enrolled 𝗘𝗻𝗿𝗼𝗹𝗹 𝗻𝗼𝘄 — 𝟯𝟰% 𝗼𝗳𝗳, 𝗲𝗻𝗱𝘀 𝘀𝗼𝗼𝗻: https://lnkd.in/eGuWr4CH | 35 comments on LinkedIn

Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design

How to leverage Large Language Models to create consistent Knowledge Graphs from multiple sources

Contribute to LMMApplication/RAKG development by creating an account on GitHub.

How AI is learning to build its own conceptual frameworks

Official Implementation of "Affordable AI Assistants with Knowledge Graph of Thoughts" - spcl/knowledge-graph-of-thoughts

🔎 Lessons Learned from Evaluating NodeRAG vs Other RAG Systems I recently dug into the NodeRAG paper (https://lnkd.in/gwaJHP94) and it was eye-opening not just for how it performed, but for what it revealed about the evolution of RAG (Retrieval-Augmented Generation) systems. Some key takeaways for me: 👉 NaiveRAG is stronger than you think. Brute-force retrieval using simple vector search sometimes beats graph-based methods, especially when graph structures are too coarse or noisy. 👉 GraphRAG was an important step, but not the final answer. While it introduced knowledge graphs and community-based retrieval, GraphRAG sometimes underperformed NaiveRAG because its communities could be too coarse, leading to irrelevant retrieval. 👉 LightRAG reduced token cost, but at the expense of accuracy. By focusing on retrieving just 1-hop neighbors instead of traversing globally, LightRAG made retrieval cheaper — but often missed important multi-hop reasoning paths, losing precision. 👉 NodeRAG shows what mature RAG looks like. NodeRAG redesigned the graph structure itself: Instead of homogeneous graphs, it uses heterogeneous graphs with fine-grained semantic units, entities, relationships, and high-level summaries — all as nodes. It combines dual search (exact match + semantic search) and shallow Personalized PageRank to precisely retrieve the most relevant context. The result? 🚀 Highest accuracy across multi-hop and open-ended benchmarks 🚀 Lowest token retrieval (i.e., lower inference costs) 🚀 Faster indexing and querying 🧠 Key takeaway: In the RAG world, it’s no longer about retrieving more — it’s about retrieving better. Fine-grained, explainable, efficient retrieval will define the next generation of RAG systems. If you’re working on RAG architectures, NodeRAG’s design principles are well worth studying! Would love to hear how others are thinking about the future of RAG systems. 🚀📚 #RAG #KnowledgeGraphs #AI #LLM #NodeRAG #GraphRAG #LightRAG #MachineLearning #GenAI #KnowledegGraphs

Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face...

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.

The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.

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

🚀 Thrilled to share our latest work published in Nature Machine Intelligence! 📄 "A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research" In this study, we constructed iKraph, one of the most comprehensive biomedical knowledge graphs to date, using a human-level information extraction pipeline that won both the LitCoin NLP Challenge and the BioCreative Challenge. iKraph integrates insights from over 34 million PubMed abstracts and 40 public databases, enabling unprecedented scale and precision in automated knowledge discovery (AKD). 💡 What sets our work apart? We developed a causal knowledge graph and a probabilistic semantic reasoning (PSR) algorithm to infer indirect entity relationships, such as drug-disease relationships. This time-aware framework allowed us to retrospectively and prospectively validate drug repurposing and drug target predictions, something rarely done in prior work. ✅ For COVID-19, we predicted hundreds of drug candidates in real-time, one-third of which were later supported by clinical trials or publications. ✅ For cystic fibrosis, we demonstrated our predictions were often validated up to a decade later, suggesting our method could significantly accelerate the drug discovery pipeline. ✅ Across diverse diseases and common drugs, we achieved benchmark-setting recall and positive predictive rates, pushing the boundaries of what's possible in drug repurposing. We believe this study sets a new frontier in biomedical discovery and demonstrates the power of structured knowledge and interpretability in real-world applications. 📚 Read the full paper: https://lnkd.in/egYgbYT4? 📌 Access the platform: https://lnkd.in/ecxwHBK7 📂 Access the data and code: https://lnkd.in/eBp2GEnH LitCoin NLP Challenge: https://lnkd.in/e-cBc6eR Kudos to our incredible team and collaborators who made this possible! #DrugDiscovery #AI #KnowledgeGraph #Bioinformatics #MachineLearning #NatureMachineIntelligence #DrugRepurposing #LLM #BiomedicalAI #NLP #COVID19 #Insilicom #NIH #NCI #NSF #ARPA-H | 10 comments on LinkedIn

Structuring legal information as a knowledge graph to increase the answer accuracy using a LangGraph agent

It seems the answer on whether an LMM would be able to replace the whole text-to-ontology pipeline is a resounding ‘no’. If you’re one of those who think that should be (or even is?) a ‘yes’: why, and did you do the experiments that show it’s as good as the alternatives (with the results available)? And I mean a proper ontology, not a knowledge graph with numerous duplications and contradictions and lacking constraints. For a few gentle considerations (and pointers to longer arguments) and a summary figure of processes the LLM supposedly would be replacing: see https://lnkd.in/dG_Xsv_6 | 43 comments on LinkedIn

Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.

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...

Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature

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

🎁⏳ Zep packs Temporal Knowledge Graphs + Semantic Entity Extraction + Cypher Queries storage—Outperforming MemGPT with 94.8% Accuracy. Build Personalized AI… | 46 comments on LinkedIn

Copyright 2025 Kurt Cagle / The Ontologist

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.

🎉 We're thrilled to unveil Synalinks (🧠🔗), an open-source framework designed to streamline the creation, evaluation, training, and deployment of…

Breaking LLM Hallucinations in a Smarter Way! (It’s not about feeding more data) Large Language Models (LLMs) still struggle with factual inaccuracies, but…

Build a knowledge graph without an LLM for your RAG applications