Why properties on relationships will kill your GraphRAG (And your vendor will not tell you why)
After seeing one too many Neo4j tutorials teaching developers to hide nouns inside relationships, I’ve had enough. Here’s why this…
A Graph RAG (Retrieval-Augmented Generation) chat application that combines OpenAI GPT with knowledge graphs stored in GraphDB
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
A free Graph / RAG system that works with real data
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
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 gentle introduction to DSPy for graph data enrichment | Kuzu
📢 Check out our latest blog post by Prashanth Rao, where we introduce the DSPy framework to help you build composable pipelines with LLMs and graphs. In the post, we dive into a fascinating dataset of Nobel laureates and their mentorship networks for a data enrichment task. 👇🏽
✅ The source data that contains the tree structures is enriched with data from the official Nobel Prize API.
✅ We showcase a 2-step methodology that combines the benefits of Kuzu's vector search capabilities with DSPy's powerful primitives to build an LLM-as-a-judge pipeline that help disambiguate entities in the data.
✅ The DSPy approach is scalable, low-cost and efficient, and is flexible enough to apply to a wide variety of domains and use cases.
A Graph-Native Workflow Application using Neo4j/Cypher | Medium
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.
GraphRAG infrastructure for generative AI using Vertex AI and Spanner Graph | Cloud Architecture Center | Google Cloud
Design infrastructure for a generative AI application with GraphRAG using Spanner Graph.
Why Knowledge Graphs are Critical to Agent Context
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.
The Developer's Guide to GraphRAG
Find out how to combine a knowledge graph with RAG for GraphRAG. Provide more complete GenAI outputs.
You’ve built a RAG system and grounded it in your own data. Then you ask a complex question that needs to draw from multiple sources. Your heart sinks when the answers you get are vague or plain wrong.
How could this happen?
Traditional vector-only RAG bases its outputs on just the words you use in your prompt. It misses out on valuable context because it pulls from different documents and data structures. Basically, it misses out on the bigger, more connected picture.
Your AI needs a mental model of your data with all its context and nuances. A knowledge graph provides just that by mapping your data as connected entities and relationships. Pair it with RAG to create a GraphRAG architecture to feed your LLM information about dependencies, sequences, hierarchies, and deeper meaning.
Check out The Developer’s Guide to GraphRAG. You’ll learn how to:
Prepare a knowledge graph for GraphRAG
Combine a knowledge graph with native vector search
Implement three GraphRAG retrieval patterns
Want to explore the Anthropic Transformer-Circuit's as a queryable graph?
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
Want to explore the Anthropic Transformer-Circuit's as a queryable graph?
Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics | Amazon Web Services
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.
Why GraphRAG Feels Hard (and What I Learned Trying to Make It Work)
Integrating Neo4j, DuckDB, and Kestra for Metadata-Driven Graph Design
Graphiti: Knowledge Graph Memory for a Post-RAG Agentic World
An open-source Python framework for real-time, graph-based knowledge in Neo4j
Google Cloud & Neo4j: Teaming Up at the Intersection of Knowledge Graphs, Agents, MCP, and Natural Language Interfaces - Graph Database & Analytics
We’re thrilled to announce new Text2Cypher models and Google’s MCP Toolbox for Databases from the collaboration between Google Cloud and Neo4j.
Everything a Developer Needs to Know About the Model Context Protocol (MCP) - Graph Database & Analytics
The Model Context Protocol gives users a wide range of services they can connect to from the comfort of their AI client.
Using Spanner Graph with LangChain for GraphRAG | Google Cloud Blog
Learn how to use Spanner Graph with LangChain workflows to build advanced, relationship-aware RAG systems, a.k.a. GraphRAG.
Talk To Your Graph 2.0 - A Partner’s View
Read about how our partners from Semantic Partners explore the latest iteration of the GraphDB's Talk To Your Graph feature
Build your hybrid-Graph for RAG & GraphRAG applications using the power of NLP | 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
Introduction to the Neo4j LLM Knowledge Graph Builder
Bridge the gap and unlock hidden potential within your unstructured data
LLM Knowledge Graph Builder — First Release of 2025
New features include community summaries, local and global retrievers, running retrievers in parallel, and custom prompt instructions.
Should you be using GraphRAG?
When (not) to use GraphRAG
Introduction to the Neo4j LLM Knowledge Graph Builder
Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.
Multimodal Graph RAG (mmGraphRAG): Incorporating Vision in Search and Analytics - Enterprise Knowledge
David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.
GraphRAG interview — Alonso
As technology advances, large language models (LLMs) and information retrieval/management methods evolve at breakneck speed. Knowledge…
LangGraph AI Agents with Neo4j Knowledge Graph
A RAG System Built for Both Semantic Search and Structured Data Queries
Introducing the Neo4j Text2Cypher (2024) Dataset
Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga
Building Knowledge Graphs with LLM Graph Transformer
A deep dive into LangChain’s implementation of graph construction with LLMs
Implementing GraphReader with Neo4j and LangGraph
Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
😎 Pretty proud to see Ontotext mentioned in more than 10 Gartner Hype Cycle reports over the last couple months! This demonstrates the success of our…
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
Microsoft GraphRAG with an RDF Knowledge Graph — Part 3
Using SPARQL and the Knowledge Graph for RAG