In this episode of Founders Discussion, TuringDB founders Adam Amara and Rémy Boutonnet sit down to discuss one of the most important and often overlooked ca...

Discover what’s new this week in graph technology, including a big acquisition, a pre-seed investment, a 360-degree take on Connected Data London, and more.

A Gremlin-based implementation of the LDBC Social Network Benchmark (SNB) Interactive v1 workload for TinkerPop-compatible graph databases.

Learn all about G.V()’s recent $658,000 pre-seed investment round from Techstart Ventures and what’s ahead for the product, company, and graph community.

TinkerBench is a benchmarking tool designed for graph databases based on Apache TinkerPop. It provides an efficient way to measure Gremlin query language performance in an easy and flexible manner. TinkerBench is created and maintained by Aerospike

Discover how to detect fraudulent financial transactions with speed and accuracy using G.V() and Aerospike Graph Database in this developer walkthrough.

Training Wheels are OFF; Graph DBs, SNA, and GraphRAG!

Building a Knowledge Graph from Meeting Notes that automatically updates

Join Hannah Bast and myself on Friday 12 December at 10am Eastern / 4pm CET to discuss “Benchmarking of Triple Stores and SPARQL engines” at https://lnkd.in/eEJr69zu

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

The global graph database market size is projected to grow from $2.85 billion in 2025 to $15.32 billion by 2032, exhibiting a CAGR of 27.1%

Zero-config Python graph database with process isolation and Unix socket communication. Build graph apps instantly without Docker or port management.

Common misconception: #Graph means I must have a #Graphdatabase.

I'm excited to announce that DeepGraph AI is open-sourcing GraphLite—the first fully open-source embedded graph database implementing the ISO GQL standard (ISO/IEC 39075:2024) in Rust.

As I’ve been diving deep into Graph RAG, one of my colleagues asked me to compare different graph databases. That got me thinking — it’s…

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

Learn more about how the latest release of G.V() now supports RDF triplestores and the SPARQL query language with autocomplete, visualization, and more.

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

Why graph databases aren’t the silver bullet some hoped for, and where the technology is heading next.

One of the most critical decisions when building a new graph database is the query language. It’s the primary interface for developers, and…

Since I'm not at #ISWC2025, it's more easy for me to speak up. There are ginormous issues with QLever and the associated Sparqloscope benchmark by Hannah Bast and colleagues. The main results table already shows something that's too good to be true. And while I'm sure that table is technically true, the tacit implication that this table has any bearing on real-world performance, is false. QLever is faster than the state of the art… at COUNTing. That's it. QLever can count faster. The implication is that this would mean QLever can also produce results faster. Yet we have zero reason to assume it can—until there's proof. In the real world, query engines rarely compute all results at once. They stream those results. The Sparqloscope benchmark is designed to trick established query engines into actually producing the result set and counting items. And you know what? Sometimes, the established engines are even faster at that than QLever, which seems to be purposefully designed to count fast. Yes—I'm sure QLever is a fast counter. But what on earth does that have to do with real-world streaming query performance? And did I mention that Virtuoso supports SPARQL UPDATE? How can you tell, just from the table? Well, Virtuoso is faster than QLever for just about anything that doesn't rely on pure counting. QLever does “Regex: prefix” or “Filter: English literals” in the ridiculously fast 0.01s? The only rational explanation is that it has a great structure for specifically this kind of counting (again, not results, just count). But Virtuoso is faster for “strbefore”? Well, there you see the real QLever performance when it cannot just count. And only one of those strategies has impact on the real world. So what if a query engine can count faster than any other to 65,099,859,287 (real result BTW). Call me when you can produce 65,099,859,287 results faster, then we'll have something to talk about. In the first place, it's a major failure of peer review that a benchmark based on COUNT was accepted. And I'd be very happy to be proven wrong: let's release the benchmark results for all engines, but without COUNT this time. Then we'll continue the conversation. https://lnkd.in/eT5XrR2k | 19 comments on LinkedIn

Azure Cosmos DB is a multi-model database service that supports various data models, including key-value, column-family, document, and…

🚀 From MSFT earnings call yesterday: Cosmos DB business grew 50% YoY in FY26 Q1! Deeply grateful for our customer's trust.❤️ Proud for Cosmos DB team to be recognized in Satya's earnings call yesterday. Also excited for our sister database SQL Hyperscale for landing nearly 75% YoY growth! Hard work leads to great results - congrats teams! Keep at it! 💪 🎉 https://lnkd.in/gGNfJtMb

We are thrilled to be the new stewards of Dgraph at Istari Digital! looking forward to building the next data platform for digital engineering.

Learn how TypeDB combines graph semantics with strong schema and reasoning to model complex domains.

Facebook, one of the world's largest social media platforms, fundamentally organizes its billions of users and their interactions as a vast social network. At the heart of this organization lies the concept of a graph—a mathematical structure consisting of nodes (or vertices) connected by edges (or

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

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