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

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

An open-source Text2SQL tool that transforms natural language into SQL using graph-powered schema understanding. Ask your database questions in plain English, QueryWeaver handles the weaving. - Fal...

Explore an iterative verification and correction refinement process aimed at improving Text2Cypher performance.

Let's chat a bit about the use of graph databases in retrieval-augmented generation (RAG). One problem in GenAI is that while the LLMs are fed a lot of text during training, perhaps a model isn't fed the specific information the user is asking about, which could be in a private corporate document. Since the dawn of GenAI, pipelines have existed to store private documents in a vector database and search for text relevant to the user's question in the database. This text is then fed to the LLM for use in generating the answer to the user query. One problem in such pipelines is that the document search may retrieve a lot of text containing terms similar to those in the user query which still isn't relevant to answering the query. At this point, many folks say, "knowledge graphs to the rescue!" Knowledge graphs after all can store information about entities mentioned in private documents, so can't they help disambiguate user questions? Graph DBs have been used in RAG for some time now; I started with them in 2021, before ChatGPT existed. There are various problems with using graph data in RAG. First off, the knowledge graphs we are trying to leverage are themselves generated by machine learning. But what are the guarantees that ML engineers are training their models or agents to produce useful KGs? Are we even using the right kind of statistical learning, never mind agent architectures? After all, if you are going to build a KG based on information in natural language, then you are parsing out conceptual relations from natural language, which are dependent on syntax. So perhaps we should be utilizing machine learning in the syntactic parsing problem, so that we ensure a relation isn't added to the graph if the syntax expresses the negation of the relation, for instance. To graph data modelers, again I maintain that methods for extracting information from syntax have more bearing on the use of graph data in RAG than existing modeling techniques that fail to factor in natural language syntax just like most ML inference fails here. And perhaps graph databases aren't even the right target for storing extracted conceptual relations; I switched to logic databases after a month of working with graphs. The use of KGs and logic bases in RAG needs to be tackled through innovations in syntax parsing like semantic grammars, and through better techniques for performant inference engines than graph query, such as GPU-native parallel inference engines. This isn't a problem I expect to be solved through Kaggle competitions or corporate R&D leveraging recently minted ML engineers.

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

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

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.

In this post, we describe how to improve the quality of the Cypher queries generated by Text2Cypher via graph schema pruning, viewed through the lens of context engineering.

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.

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

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.

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

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

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.

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

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

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

Bridge the gap and unlock hidden potential within your unstructured data

New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.

When (not) to use GraphRAG

Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.

Enabling an LLM to chat directly with structured graph data

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.

Discover Neo4j's 2024 product innovations in cloud-native graph technology, enhancing performance, scalability, AI innovation, and developer productivity.