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Most Graph RAG discussions are still around 𝘸𝘩𝘪𝘤𝘩 𝘨𝘳𝘢𝘱𝘩 𝘥𝘢𝘵𝘢𝘣𝘴𝘦 𝘵𝘰 𝘶𝘴𝘦? Serious architecture asks: Which graph structure fits the retrieval job? That is the Graph-Type Fit Pattern. The mistake is thinking “graph” is a feature.

Gartner D&A is always a mix of signal and noise.

A daily-refreshed catalog of knowledge graph resources built on Wikidata

Modern data architectures are fragmented across graph databases, vector stores, analytics engines, and optimization solvers, resulting in complex ETL pipelines and synchronization overhead. We...

Retrieval-Augmented Generation (RAG) systems combine Large Language Models (LLMs) with external knowledge, and their performance depends heavily on how that knowledge is represented. This study...

In this post, I want to draw attention to what I think is a very important, yet under-appreciated, aspect of the Semantic Web. Let's start with a brief intro to a latter, for whom is not familiar with it.

Browse ontologies, controlled vocabularies, taxonomies, and semantic software from a daily-refreshed RDF catalog.

Over the past several years I’ve been thinking about how AI systems will actually operate inside real enterprises. Most discussions focus on models, pipelines, or agents. Those things matter, but they only describe parts of the system. Enterprises don’t operate on probability alone. They operate within defined objectives, authority structures, and governance processes. That raises a different architectural question. How should probabilistic AI reasoning operate inside institutions that ultimately make decisions through governed processes? The article below outlines a framework I’ve been developing called the AI Knowledge Framework (AIKF). It connects five parts of enterprise AI architecture that are often discussed separately: - Purpose - Meaning - Evidence - Reasoning - Governance The central idea is straightforward. AI systems are good at discovering patterns and generating evidence. Institutions remain responsible for determining meaning, responsibility, and consequences. Purpose defines the boundaries within which AI reasoning operates. I’d be interested in hearing how others working on enterprise ontologies, knowledge graphs, semantic architectures, or governed AI systems are thinking about these issues. | 43 comments on LinkedIn

Visualise any GitHub repo as a graph. Simply replace "github" with "gitcgr" in the repo URL. This gives you an interactive graph of the entire codebase structure, with functions, classes, modules, and their relationships all laid out visually. Internally, it's powered by Code-Graph-RAG, an open-source system that does deep code analysis under the hood: - It uses Tree-sitter to parse your codebase across multiple languages. - Builds a full knowledge graph of your repo in Memgraph. - Lets you query your codebase in natural language. Code-Graph-RAG also works as an MCP server, which means you can plug it directly into Claude Code and query or edit your codebase using natural language from your terminal. ___ Interested in ML/AI Engineering? I have put together a free PDF (380+ pages) with 150+ core lessons. Find it in the comments! ___ Share this with your network if you found this insightful ♻️ Find me → Avi Chawla. Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs. | 10 comments on LinkedIn

Is Palantir onto something, or are they just blowing hot air? You may have heard the term Ontology, and why leading businesses rely on them to build trustworthy data models. Recent advances in LLMs have made it possible for startups and enterprises alike to build flexible, reliable semantic layers faster

Graphs are a powerful way to model complex relationships, from social networks to supply chains.

People are conflating data context and decision context.

From Berlin to San Francisco On the same day, two technical communities one in Berlin and one in San Francisco gathered to discuss the same architectural question: how should memory be designed for agentic AI systems? In Berlin, AI Memory and… Read more →

Learn how context graphs capture decision reasoning and situational awareness to power AI agents with episodic memory. Build production-ready context graphs with FalkorDB.

Let's roll up our sleeves and follow up on my last post in some more detail. The many qualitatively different types of relations in a rich knowledge graph are the crux of how it adds significant value above and beyond a list of categories or a taxonomy.

Budget for Semantic Capabilities is Non-Negotiable! Music to my ears!! Scratching your head on how to start? Take a look at the 20 lessons learned from 20 years of building knowledge graphs and ontologies. https://lnkd.in/g_y5KMVm This may be new to you. But there is a large community who have been working with semantics and doing this in production for a while. For example, see all the companies that presented at Connected Data London and The Knowledge Graph Conference last year. Take a look at my trip report for more details on those companies and what they are doing https://lnkd.in/gFQZQhyE You don’t have to be on this semantic journey by yourself. This is moment to leverage the community!

Crack open your codebase RAG with a knowledge graph analysis that illustrates hidden inheritance chains, hub detection, and more in this 2-part blog series.

Enterprise Data Architecture for the Agentic Era, Part 3 of 3

KuzuDB was archived in October 2025. Vela Partners forked it with concurrent write support for multi-agent workloads. Benchmarks show 374x faster path queries than Neo4j. Full architecture guide for AI agent memory on graph databases.

Neo4j databases are famous for being schema-optional and developers love the flexibility this gives them to get up and running fast. But eventually, it’s time to go to production… and you need rules. You need structure. You need to enforce… Read more →

Before Shannon, "information" meant whatever people wanted it to mean — knowledge, facts, news, data.

Why Natural Language Interfaces to Graphs Often Fail and What Graph Architecture Has to Do With It