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.

For years, I considered graph databases “interesting but niche.” Relevant commercially for social networks, supply chain and academically for biotech, maybe some knowledge management. Basically, not something most companies would ever need. I stand corrected. With AI, they’re having a very big moment! Working with graphs the first time feels unusual but also just right. The best analogy I have is that feeling we get when we try to visualize a higher dimension when all we have ever known are three (+ time for the purists). (or is it just me?) Two use-cases that I have been riffing on: * Knowledge management: For me it started as a personal project for personal knowledge management. For enterprises, this is where RAG shines. But I also wonder if there are other applications within Enterprise Knowledge Management that we aren’t thinking of yet.  * Master Data Management (MDM): Potentially a subset of above, but explicitly about attributes and relationships that columnar databases might handle too rigidly. I am a lifetime subscriber for relational and SQL till they exist. Not saying they will go away. Graphs still feel intuitive and unusual at the same time. They are still complex to build (although companies like Neo4j simplify them really well), and difficult to traverse / interpret. I believe there is a stronger convergence of these 2 systems coming. Graphs will augment relational before replacing in some of these use-cases. But they have to be way more simplified first for greater adoption. Would love to hear more from graph experts and/or from those who share this feeling of “just right” for graphs. Are you seeing use-cases where graph databases are picking up? #AI #DataStrategy #Graphs #KnowledgeManagement #MDM | 37 comments on LinkedIn

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

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

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.

Every year, businesses and consumers lose billions of dollars to fraud, with consumers reporting $12.5 billion lost to fraud in 2024, a 25% increase year over year. People who commit fraud often work together in organized fraud networks, running many different schemes that companies struggle to detect and stop. In this post, we discuss how to use Amazon Neptune Analytics, a memory-optimized graph database engine for analytics, and GraphStorm, a scalable open source graph machine learning (ML) library, to build a fraud analysis pipeline with AWS services.

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

Picture an AI agent that seamlessly traverses knowledge graphs while performing semantic vector searches, applies probabilistic predictions alongside deterministic rules, reasons about temporal evolution and spatial relationships, and resolves contradictions between multiple data sources—all within a single atomic transaction. It is PostgreSQL-based architecture that consolidates traditionally distributed data systems into a single, coherent platform. This architecture doesn't just store different data types; it enables every conceivable form of reasoning—deductive, inductive, abductive, analogical, causal, and spatial—transforming isolated data modalities into a coherent intelligence substrate where graph algorithms, embeddings, tabular predictions, and ontological inference work in perfect harmony. It changes how agentic systems operate by eliminating the complexity and inconsistencies inherent in multi-database architectures while enabling sophisticated multi-modal reasoning capabilities. Conventional approaches typically distribute agent knowledge across multiple specialized systems: vector databases for semantic search, graph databases for relationship reasoning, relational databases for structured data, and separate ML platforms for predictions. This fragmentation creates synchronization nightmares, latency penalties, and operational complexity that can cripple agent performance and reliability. Apache AGE brings native graph database capabilities to PostgreSQL, enabling complex relationship traversal and graph algorithms without requiring a separate graph database. Similarly, pgvector enables semantic search through vector embeddings, while extensions like TabICL provide zero-shot machine learning predictions directly within the database. This extensibility allows PostgreSQL to serve as a unified substrate for all data modalities that agents require. While AGE may not match the pure performance of dedicated graph databases like Neo4j for certain specialized operations, it excels in the hybrid queries that agents typically require. An agent rarely needs just graph traversal or just vector search; it needs to combine these operations with structured queries and ML predictions in coherent reasoning chains. The ability to perform these operations within single ACID transactions eliminates entire classes of consistency bugs that plague distributed systems. Foundational models eliminate traditional ML complexity. TabICL and TabSTAR enable instant predictions on new data patterns without training, deployment, or complex MLOps pipelines. This capability is particularly crucial for agentic systems that must adapt quickly to new situations and data types without human intervention or retraining cycles. The unified architecture simplifies every aspect of system management: one backup strategy instead of multiple, unified security through PostgreSQL's mature RBAC system, consistent monitoring, and simplified debugging. | 21 comments on LinkedIn

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.

Graph Data Modeling Without Graph Databases: PostgreSQL and Hybrid Approaches for Agentic Systems 🖇️ Organizations implementing AI systems today face a practical challenge: maintaining multiple specialized databases (vector stores, graph databases, relational systems) creates significant operational complexity, increases costs, and introduces synchronization headaches. Companies like Writer (insight from a recent Waseem Alshikh interview with Harrison Chase) have tackled this problem by implementing graph-like structures directly within PostgreSQL, eliminating the need for separate graph databases while maintaining the necessary functionality. This approach dramatically simplifies infrastructure management, reduces the number of systems to monitor, and eliminates error-prone synchronization processes that can cost thousands of dollars in wasted resources. For enterprises focused on delivering business value rather than managing technical complexity, these PostgreSQL-based implementations offer a pragmatic path forward, though with important trade-offs when considering more sophisticated agentic systems. Writer implemented a subject-predicate-object triple structure directly in PostgreSQL tables rather than using dedicated graph databases. This approach maintains the semantic richness of knowledge graphs while leveraging PostgreSQL's maturity and scalability. Writer kept the conceptual structure of triples that underpin knowledge graphs implemented through a relational schema design. Instead of relying on native graph traversals, Writer developed a fusion decoder that reconstructs graph-like relationships at query time. This component serves as the bridge between the storage layer (PostgreSQL with its triple-inspired structure) and the language model, enabling sophisticated information retrieval without requiring a dedicated graph database's traversal capabilities. The approach focuses on query translation and result combination rather than storage structure optimization. Complementing the triple-based approach, PostgreSQL with extensions (PG Vector and PG Vector Scale) can function effectively as a vector database. This challenges the notion that specialized vector databases are necessary, Treating embeddings as derived data leads to a more natural and maintainable architecture. This reframes the database's role from storing independent vector embeddings to managing derived data that automatically synchronizes with its source. But a critical distinction between retrieval systems and agentic systems need to be made. While PostgreSQL-based approaches excel at knowledge retrieval tasks where the focus is on precision and relevance, agentic systems operate in dynamic environments where context evolves over time, previous actions influence future decisions, and contradictions need to be resolved. This distinction drives different architectural requirements and suggests potential complementary roles for different database approaches. | 15 comments on LinkedIn

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

Imagine you’re solving a complex puzzle with scattered pieces of information.