After seeing one too many Neo4j tutorials teaching developers to hide nouns inside relationships, I’ve had enough. Here’s why this…

The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.

After seeing yet another Graph RAG demo using Neo4j with no ontology, I decided to show what real semantic Graph RAG looks like. The Problem with Most Graph RAG Demos: Everyone's building Graph RAG with LPG databases (Neo4j, TigerGraph, Arrango etc.) and calling it "knowledge graphs." But here's the thing: Without formal ontologies, you don't have a knowledge graph—you just have a graph database. The difference? ❌ LPG: Nodes and edges are just strings. No semantics. No reasoning. No standards. ✅ RDF/SPARQL: Formal ontologies (RDFS/OWL) that define domain knowledge. Machine-readable semantics. W3C standards. Built-in reasoning. So I Built a Real Semantic Graph RAG Using: - Microsoft Agent Framework - AI orchestration - Formal ontologies - RDFS/OWL knowledge representation - Ontotext GraphDB - RDF triple store - SPARQL - semantic querying - GPT-5 - ontology-aware extraction It's all on github, a simple template as boilerplate for you project: The "Jaguar problem": What does "Yesterday I was hit by a Jaguar" really mean? It is impossible to know without concept awareness. To demonstrate why ontologies matter, I created a corpus with mixed content: 🐆 Wildlife jaguars (Panthera onca) 🚗 Jaguar cars (E-Type, XK-E) 🎸 Fender Jaguar guitars I fed this to GPT-5 along with a jaguar conservation ontology. The result? The LLM automatically extracted ONLY wildlife-related entities—filtering out cars and guitars—because it understood the semantic domain from the ontology. No post-processing. No manual cleanup. Just intelligent, concept-aware extraction. This is impossible with LPG databases because they lack formal semantic structure. Labels like (:Jaguar) are just strings—the LLM has no way to know if you mean the animal, car, or guitar. Knowledge Graphs = "Data for AI" LLMs don't need more data—they need structured, semantic data they can reason over. That's what formal ontologies provide: ✅ Domain context ✅ Class hierarchies ✅ Property definitions ✅ Relationship semantics ✅ Reasoning rules This transforms Graph RAG from keyword matching into true semantic retrieval. Check Out the Full Implementation, the repo includes: Complete Graph RAG implementation with Microsoft Agent Framework Working jaguar conservation knowledge graph Jupyter notebook: ontology-aware extraction from mixed-content text https://lnkd.in/dmf5HDRm And if you have gotten this far, you realize that most of this post is written by Cursor ... That goes for the code too. 😁 Your Turn: I know this is a contentious topic. Many teams are heavily invested in LPG-based Graph RAG. What are your thoughts on RDF vs. LPG for Graph RAG? Drop a comment below! #GraphRAG #KnowledgeGraphs #SemanticWeb #RDF #SPARQL #AI #MachineLearning #LLM #Ontology #KnowledgeRepresentation #OpenSource #neo4j #graphdb #agentic-framework #ontotext #agenticai | 148 comments on LinkedIn

Calling all Graph Explorers! 📣 I'm excited to share that open-source Graph Explorer v2.4.0 is now released, and it includes a new SPARQL editor! Release notes: https://lnkd.in/ePhwPQ5W This means that in addition to being a powerful no-code exploration tool, you can now start your visualization and exploration by writing queries directly in SPARQL. (Gremlin & openCypher too for Property Graph workloads). This makes Graph Explorer an ideal companion for Amazon Neptune, as it supports connections via all three query languages, but you can connect to other graph databases that support these languages too. 🔹 Run it anywhere (it's open source): https://lnkd.in/ehbErxMV 🔹 Access through the AWS console in a Neptune graph notebook: https://lnkd.in/gZ7CJT8D Special thanks go to Kris McGinnes for his efforts. #AWS #AmazonNeptune #GraphExplorer #SPARQL #Gremlin #openCypher #KnowledgeGraph #OpenSource #RDF #LPG

A sophisticated knowledge graph memory system that stores interconnected information with rich semantic structure using Neo4j. - shuruheel/mcp-neo4j-shan

I’ve tested countless Retrieval-Augmented Generation (RAG) systems, and they all failed to meet my expectations. I found a simple two-step…

Knowledge graphs is a powerful concept where we can represent any flow or entity relationships in a structured way.

𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 is now flexing to the max using LlamaIndex, supports 𝟳 𝗴𝗿𝗮𝗽𝗵 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟬 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝟭𝟯 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀, 𝗟𝗟𝗠𝘀, Docling 𝗱𝗼𝗰 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴, 𝗮𝘂𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗞𝗚𝘀, 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚, 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵, 𝗔𝗜 𝗖𝗵𝗮𝘁 (shown Hyland products web page data src) 𝗔𝗽𝗮𝗰𝗵𝗲 𝟮.𝟬 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗚𝗿𝗮𝗽𝗵: Neo4j ArcadeDB FalkorDB Kuzu NebulaGraph, powered by Vesoft (coming Memgraph and 𝗔𝗺𝗮𝘇𝗼𝗻 𝗡𝗲𝗽𝘁𝘂𝗻𝗲) 𝗩𝗲𝗰𝘁𝗼𝗿: Qdrant, Elastic, OpenSearch Project, Neo4j 𝘃𝗲𝗰𝘁𝗼𝗿, Milvus, created by Zilliz (coming Weaviate, Chroma, Pinecone, 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 + 𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿, LanceDB) Docling 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀: using LlamaIndex readers: working: Web Pages, Wikipedia, Youtube, untested: Google Drive, Msft OneDrive, S3, Azure Blob, GCS, Box, SharePoint, previous: filesystem, Alfresco, CMIS. 𝗟𝗟𝗠𝘀: 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 𝗟𝗟𝗠𝘀 (OpenAI, Ollama, Claude, Gemini, etc.) 𝗥𝗲𝗮𝗰𝘁, 𝗩𝘂𝗲, 𝗔𝗻𝗴𝘂𝗹𝗮𝗿 𝗨𝗜𝘀, 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿, 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝘀𝗲𝗿𝘃𝗲𝗿 𝗚𝗶𝘁𝗛𝘂𝗯 𝘀𝘁𝗲𝘃𝗲𝗿𝗲𝗶𝗻𝗲𝗿/𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲-𝗴𝗿𝗮𝗽𝗵𝗿𝗮𝗴: https://lnkd.in/eUEeF2cN 𝗫.𝗰𝗼𝗺 𝗣𝗼𝘀𝘁 𝗼𝗻 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 𝗼𝗿 𝗥𝗔𝗚 𝗺𝗮𝘅 𝗳𝗹𝗲𝘅𝗶𝗻𝗴 https://lnkd.in/gHpTupAr 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 𝗕𝗹𝗼𝗴: https://lnkd.in/ehpjTV7d

I am excited to announce the formation of a Data Façades W3C Community Group. Façade-X, initially introduced at SEMANTICS 2021 and successfully implemented by the SPARQL Anything project, provides a simple yet powerful, homogeneous view over diverse and heterogeneous data sources (e.g., CSV, JSON, XML, and many others). With the recent v1.0.0 release of SPARQL Anything, the time was right to work on the long-term stability and widespread adoption of this approach by developing an open, vendor-neutral technology. The Façade-X concept was born to allow SPARQL users to query data in any structured format in plain SPARQL. Therefore, the choice of a W3C community group to lead efforts on specifications is just natural. Specifications will enhance its reliability, foster innovation, and encourage various vendors and projects—including graph database developers — to provide their own compatible implementations. The primary goals of the Data Façades Community Group is to: Define the core specification of the Façade-X method. Define Standard Mappings: Formalize the required mappings and profiles for connecting Façade-X to common data formats. Define the specification of the query dialect: Provide a reference for the SPARQL dialect, configuration conventions (like SERVICE IRIs), and the functions/magic properties used. Establish Governance: Create a monitored, robust process for adding support for new data formats. Foster Collaboration: Build connections with relevant W3C groups (e.g., RDF & SPARQL, Data Shapes) and encourage involvement from developers, businesses, and adopters. Join us! With Luigi Asprino Ivo Velitchkov Justin Dowdy Paul Mulholland Andy Seaborne Ryan Shaw ... CG: https://lnkd.in/eSxuqsvn Github: https://lnkd.in/dkHGT8N3 SPARQL Anything #RDF #SPARQL #W3C #FX

The Linked Data Benchmark Council has, for over a decade, been the quiet force behind much of the progress in graph technology. Their mission is deceptively simple: to design, maintain, and promote standard benchmarks for graph data management systems. Read about the recent meeting and the announcem

Calling all data scientists, developers, and managers! 📢 Looking to level up your team's knowledge of graph technology? We're excited to share the recorded 2-part training series, "Graph Tech Demystified" with the amazing Paco Nathan. This is your chance to get up to speed on graph fundamentals: In Part 1: Intro to Graph Technologies, you'll learn: - Core concepts in graph tech. - Common pitfalls and what graph technology won't solve. - Focus of graph analytics and measuring quality. 🎥 Recording https://lnkd.in/gCtCCZH5 📖 Slides https://lnkd.in/gbCnUjQN In Part 2: Advanced Topics in Graph Technologies, we explore: - Sophisticated graph patterns like motifs and probabilistic subgraphs. - Intersection of Graph Neural Networks (GNNs) and Reinforcement Learning. - Multi-agent systems and Graph RAG. 🎥 Recording https://lnkd.in/g_5B8nNC 📖 Slides https://lnkd.in/g6iMbJ_Z Insider tip: The resources alone are enough to keep you busy far longer the time it takes to watch the training!

It's great to see the launch of Google Cloud's new Agentspace Knowledge Graph, built on Spanner Graph. Agentspace Knowledge Graph (https://lnkd.in/gYM6xZQS) allows an AI agent to understand the real-world context of your organization—the web of relationships between people, projects, and products. This is the difference between finding a document and understanding who wrote it, what team they're on, and what project it's for. Because this context is a network, the problem is uniquely suited for a graph model. Spanner Graph (https://lnkd.in/gkwbGFbS) provides a natural way to model this reality, allowing an AI agent to instantly traverse complex connections to find not just data, but genuine insight. This is how we move from AI that finds information to AI that understands it. The ability to reason over the "why" behind the data is a true game-changer. #GoogleCloud #GenAI #Agentspace #SpannerGraph #KnowledgeGraph

I recently came across some misconceptions about why the LPG graph model is more effective than RDF for GraphRAG, and I wrote this article to debunk them. At the end, I also elaborate on two principal advantages of RDF when it comes to provision of context and grounding to LLMs (i) schema languages

𝗖𝗮𝗻 𝗮 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗕𝗲 𝗮 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵? Not all knowledge graphs are equal. A semantic KG (RDF/OWL/Stardog) isn’t the same as a property graph (Neo4j), and both differ from enforcing graph-like structures in a relational DB (CockroachDB/Postgres). Each has strengths and trade-offs: 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗚𝘀 excel at reasoning and inference over ontologies. 𝗣𝗿𝗼𝗽𝗲𝗿𝘁𝘆 𝗴𝗿𝗮𝗽𝗵𝘀 shine when exploring relationships with intuitive query patterns. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀 enforce graph-like models via schema, FKs, indexes, recursive CTEs — with added benefits of scale, distributed TXs, and decades of maturity. 𝗚𝗿𝗮𝗽𝗵 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹𝘀 𝗶𝗻 𝗦𝗤𝗟 Recursive CTEs let SQL “walk the graph.” Start with a base case (movie + actors), then repeatedly join back to discover multi-hop paths (actors → movies → actors → movies). This simulates “friends-of-friends” traversals in a few lines of SQL. 𝗥𝗔𝗚, 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚, 𝗮𝗻𝗱 𝗟𝗟𝗠𝘀 RAG and GraphRAG give LLMs grounding in structured data, reducing hallucinations and injecting context. Whether via RDF triples, LPG edges, or SQL joins — the principle is the same: real relationships fuel better answers. 𝗧𝗵𝗲 𝟯-𝗛𝗼𝗽 𝗔𝗿𝗴𝘂𝗺𝗲𝗻𝘁 Some vendors claim SQL breaks down after 3 hops. In reality, recursive CTEs traverse arbitrary depth. SQL may not be as compact as Cypher or GQL, but it’s expressive and efficient — the “3-hop wall” is outdated FUD. 𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 One graph DB is notorious for slow, resource-heavy CSV loads. Distributed RDBMS like CockroachDB can bulk ingest 100s of GB to TBs efficiently. 𝗡𝗼 𝗦𝘁𝗮𝗹𝗲 𝗗𝗮𝘁𝗮 Too often, data must move from TX systems into a graph before use — by then, it’s stale. For AI-driven apps, that lag means hallucinations, missed insights, and poor UX. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗔𝗜 As AI apps go multi-regional and global, they demand low latency + strong consistency. Centralized graph DBs hit lag, hotspots, scaling pain. Distributed SQL delivers expressive queries and global consistency — exactly what AI workloads need. You don’t need to pick “graph” or “relational” as religion. Choose the right model for scale, consistency, and AI grounding. Sometimes RDF. Sometimes LPG. And sometimes, graph-enforced in SQL. #KnowledgeGraph #ArtificialIntelligence #GenerativeAI #DistributedSQL #CockroachDB | 11 comments on LinkedIn

Looking into building knowledge graphs? Check out this code tutorial on how we built a knowledge graph of the latest 'La Liga' standings! ⚽️👩‍💻 Google Coll...

Workshop from @FalkorDB and ZEP (Graphiti): Building Production Knowledge Graphs from Structured/Unstructured Data Sources.👩‍💻 Google Collab for the demo:...

In case you were wondering what I have been up to lately, Hydra is a large part of it. This is the open source graph programming language I alluded to last year at the Knowledge Graph Conference. Hydra is almost ready for its 1.0 release, and I am planning on making it into a community project, possibly through the Apache Incubator. In this initial demo video, we take an arbitrary tabular dataset and use Hydra + Claude to map it into a property graph. More specifically, we use the LLM once to construct a pair of schemas and a mapping. From there, we apply the mapping deterministically and efficiently to each row of data, without additional calls to the LLM. The recording was a little too long for LinkedIn, so I broke it into two parts. I will post part 2 momentarily (edit: part 2 is here: https://lnkd.in/gZmHicXu). More videos will follow as we get closer to the release. GitHub: https://lnkd.in/g8v2hvd5 Discord: https://bit.ly/lg-discord

Building Enterprise Knowledge Graphs Within Modern Data Platforms - Version 26 Louie Franco III Enterprise Architect - Knowledge Graph Architect - Semantics Architect August 3, 2025 In my previous article on Data Vault Medallion Architecture, I outlined how structured data flows through Landing, Bro

Our SPARQL Notebook extension for Visual Studio Code makes it super easy to document SPARQL queries and run them, either against live endpoints or directly on local RDF files. I just (finally!) published a 15-minute walkthrough on our YouTube channel Giant Global Graph. It gives you a quick overview of how it works and how you can get started. Link in the comments. Fun fact: I recorded this two years ago and apparently forgot to hit publish. Since then, we've added new features like improved table renderers with pivoting support, so it's even more useful now. Check it out! | 11 comments on LinkedIn

A full working Cypher script that simulates a Tendering System with multiple workflows, AI agent interactions, conversations, approvals, and more — all modeled and executed natively in a Graph.

Nice piece on the comparisons of Vector DBs vs Knowledge Graphs. I think this becomes even more true when you start talking about temporal knowledge graphs, in which you are effectively describing temporal causality and contingent assertions.

Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.

How should we organize knowledge to provide the best context for agents? We show how knowledge graphs could play a key role in enhancing context for agents.

Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.

The tech industry has a voracious appetite for the Next Big Thing. But sometimes, it’s the older thing that ends up being the right tool for a new job.

Transform a relational database into a knowledge graph using cognee and Kuzu, enabling advanced querying and semantic search on your data.

Want to explore the Anthropic Transformer-Circuit's as a queryable graph? Wrote a script to import the graph json into Neo4j - code in Gist. https://lnkd.in/eT4NjQgY https://lnkd.in/e38TfQpF Next step - write directly from the circuit-tracer library to the graph db. https://lnkd.in/eVU_t6mS

Spacetime and information both have the basics of a geometry

6 years after, where are we? Interested by your feedback concerning the evolution of the graph technologies landscape and about what the current landscape is. https://lnkd.in/eEPkExH | 25 comments on LinkedIn