Semantic Data in Medallion Architecture: Enterprise Knowledge Graphs at Scale | LinkedIn
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
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.
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
Getting Started with the Graph Query Language (GQL): The complete guide to designing, querying, and managing graph databases with GQL: 9781836204015: Computer Science Books @ Amazon.com
Getting Started with the Graph Query Language (GQL): The complete guide to designing, querying, and managing graph databases with GQL: 9781836204015: Computer Science Books @ Amazon.com
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.
Improving Text2Cypher for Graph RAG via schema pruning | Kuzu
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.
TigerGraph Accelerates Enterprise AI Infrastructure Innovation with Strategic Investment from Cuadrilla Capital - TigerGraph
TigerGraph secures a strategic investment from Cuadrilla Capital to fuel innovation in enterprise AI infrastructure and graph database technology, delivering advanced solutions for fraud detection, customer 360, supply chain optimization, and real-time data analytics.
Nice piece on the comparisons of Vector DBs vs Knowledge Graphs.
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.
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.
Use Graph Machine Learning to detect fraud with Amazon Neptune Analytics and GraphStorm | Amazon Web Services
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.
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
‘The Relational Model Always Wins,' RelationalAI CEO Says
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.
Unified graph architecture for Agentic AI based on Postgres and Apache AGE
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
Discover & Visualize Your Graph Database Schema in Just 2 Steps
Stop wondering about changes to your graph data model and start exploring your up-to-date database schema with only a couple of clicks when you use G.V().
In our GraphGeeks Talk with Max De Marzi , we unpack what makes a graph model solid, what tends to break things, and how to design with both your data and your queries in mind.
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?
