Earlier this year, I discovered Larry Swanson podcast on Knowledge Graphs. One of the first episodes I listened to was with George Anadiotis Six months later, I finally met George in person. And not just George! Amy Hodler from GraphGeeks was there too, hosting an excellent event on graph technologies. I’m still quite new to the graph tech world. My initial interest came from exploring how graphs can support legal reasoning and inference in LLMs, mainly because graphs help introduce logic, determinism, and reduce hallucinations. But this event (and the people there) helped me understand how much broader the applications really are. One of the first things I learned was that graph technologies largely fall into two families: Label-Property Graphs (LPG) and RDF. With the help of the experts onsite, I explored the most common use cases for each. For LPG (structured knowledge): • Pattern recognition – Finance: fraud detection, anti-fraud behavior chains (something SQL can’t trace) – Compliance & risk: the London Stock Exchange uses LPG to trace paths leading to risk concentration for DORA compliance • Route / path finding – Cybersecurity: mapping exploit paths – Supply chain: modelling supply routes and comparing alternatives – Incident analysis: understanding causal chains inside complex systems For RDF (built for meaning and semantics): • Domain modeling and knowledge engineering • AI memory architectures There were also discussions about hybrid approaches where both frameworks work together: Natural language query → grounded semantically with RDF → executed through an LPG engine. In practice, this looks like: LLMs providing the interface, RDF providing the semantics, and LPG providing the performance. A powerful combination for building the next generation of intelligent systems. Thank you George, Amy, Maja and everyone else for the insights and conversations. And thanks to GraphGeeks and Connected Data for bringing such a strong community together.

Summary of the things I learned at the 2025 International Semantic Web Conference, my scientific home.

Summary of what I learned at Connected Data London (CDL2025). The winners are clear: companies who have quietly been investing in knowledge graphs for the last decade, way before this current AI wave

This text is about my impressions from Connected Data London 2024. And about working towards a shared space of present and possible collaborative actions based on connected data and content. Intro: Shiny Happy Data People 20 years after the article in which Sir Tim Berners Lee imagined a paper on which you can click withContinue Weaving

As organizations increasingly prioritize linked data capabilities to connect information across the enterprise, selecting the right graph framework to leverage has become more important than ever. In this webinar, graph technology experts from Enterprise Knowledge Elliot Risch, James Egan, David Hughes, and Sara Nash shared the best ways to manage and apply a selection of these frameworks to meet enterprise needs.

❓ How can you turn business questions into production-ready agentic knowledge graphs? Join Prashanth Rao and Dennis Irorere at the Agentic AI Summit to find out. Prashanth is an AI Engineer and DevRel lead at Kùzu Inc.—the open-source graph database startup—where he blends NLP, ML, and data engineering to power agentic workflows. Dennis is a Data Engineer at Tripadvisor’s Viator Marketing Technology team and Director of Innovation at GraphGeeks, driving scalable, AI-driven graph solutions for customer growth. In “Agentic Workflows for Graph RAG: Building Production-Ready Knowledge Graphs,” they’ll guide you through three hands-on lessons: 🔹 From Business Question to Graph Schema – Modeling your domain for downstream agents and LLMs, using live data sources like AskNews. 🔹 From Unstructured Data to Agent-Ready Graphs with BAML – Writing declarative pipelines that reliably extract entities and relationships at scale. 🔹 Agentic Graph RAG in Action – Completing the loop: translating NL queries into Cypher, retrieving graph data, and synthesizing responses—with fallback strategies when matches are missing. If you’re building internal tools or public-facing AI agents that rely on knowledge graphs, this workshop is for you. 🗓️ Learn more & register free: https://hubs.li/Q03qHnpQ0 #AgenticAI #GraphRAG #KnowledgeGraphs #AgentWorkflows #AIEngineering #ODSC #Kuzu #Tripadvisor

📣 AI Engineer World's Fair 2025: GraphRAG Track Spotlight! 🚀 So grateful to have hosted the GraphRAG Track at the Fair. The sessions were great, highlighting the depth and breadth of graph thinking for AI. Shoutouts to... - Mitesh Patel "HybridRAG" as a fusion of graph and vector retrieval designed to master complex data interpretation and specialized terminology for question answering - Chin Keong Lam "Wisdom Discovery at Scale" using Knowledge Augmented Generation (KAG) in a multi agent system with n8n - Sam Julien "When Vectors Break Down" carefully explaining how graph-based RAG architecture achieved a whopping 86.31% accuracy for dense enterprise knowledge - Daniel Chalef "Stop Using RAG as Memory" explored temporally-aware knowledge graphs, built by the open-source Graphiti framework, to provide precise, context-rich memory for agents, - Ola Mabadeje "Witness the power of Multi-Agent AI & Network Knowledge Graphs" showing dramatic improvements in ticket resolution efficiency and overall execution quality in network operations. - Thomas Smoker "Beyond Documents"! casually mentioning scraping the entire internet to distill a knowledge graph focused with legal agents - Mark Bain hosting an excellent Agentic Memory with Knowledge Graphs lunch&learn, with expansive thoughts and demos from Vasilije Markovic Daniel Chalef and Alexander Gilmore Also, of course, huge congrats to Shawn swyx W and Benjamin Dunphy on an excellent conference. 🎩 #graphrag Neo4j AI Engineer

Last week, I was happy to be able to attend the 22nd European Semantic Web Conference. I’m a regular at this conference and it’s great to see many friends and colleagues as well as meet…

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/

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