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For newcomers into the world of semantic technology and knowledge graphs, the diagram above illustrates some of the key languages that you may want to look into. RDF RDF defines the very lowest level building blocks of how graphs can be represented.

A collection of Graph Embedding methods in Python. 🧠💎 This repository provides hands-on implementations of essential graph embedding algorithms like: ▪️…

Wonderful debate here. Let’s set the record straight—if you’re still stuck thinking schema markup is just a checkbox for rich results, you’re not only missing the point but also losing the game.

Want better results from your RAG? GraphRAG takes it to the next level. GraphRAG is a powerful approach to retrieval augmented generation (RAG). It… | 46 comments on LinkedIn

Turn your Pandas Data Frame into a Knowledge Graph using LLMs. Build your own LLM graph-builder and QA your KG.

Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms:

Parsing through hype to understand how to think about Knowledge Graph creation processes

Graph is increasingly driving the Agentic space, which I see as being a very good sign. Recently, a programmer asked why graphs would be superior to using…

Dear LinkedIn Fam, We need to have a conversation about something… Label Property Graphs (LPGs) are not ontological-based knowledge graphs because they lack…

Watch the first podcast of Paco Nathan's Graph Power Hour. This week's topic - Understanding Graph Rag: Enhancing LLM Applications Through Knowledge Graphs.

The Power of Graph-Native Intelligence for Agentic AI Systems How Entity Resolution, Knowledge Fusion, and Extension Frameworks Transform Enterprise AI ⚡…

I want to emphasize some things that most people don't seem to understand, specially managers in the AI space. 1. Knowledge Graph/Ontologies without a way to… | 14 comments on LinkedIn

Working with RDF on LLMs ================== The following is a quick reference list of things I've found when trying to work with the RDF stack on LLMs *… | 14 comments on LinkedIn

Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 11 comments on LinkedIn

Making LLM Knowledge Graph building fast & dynamic: challenges, design decisions, and solutions

Image by David from Pixabay Mobile phones make it possible to secure and manage personal data on-device, which opens up a novel opportunity for both phone owners and device manufacturers: AI personalization via a data resource that stays on the phone. With the right design, personal knowledge graph on-device could provide contextualization while at the… Read More »Samsung and Apple’s knowledge-centric approaches to secure, personalized AI on phones

At Microsoft, we strive to constantly learn and share our insights across innovation, culture and governance. Today, as we reflect on our progress since launching the Secure Future Initiative one year ago, I want to share a little more about our vision for a graph-powered platform future.

WhyHow’s Knowledge Graph Studio for modular agentic RAG systems.

My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use of…

I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request with… | 63 comments on LinkedIn

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy ⛳ In the realm of agentic systems, a fundamental challenge emerges…

📑 I’ve been out of the grid the past few weeks to wrap up my upcoming book. 🚀 After 12 months of work that turned out to be more intense than I expected… | 20 comments on LinkedIn

A Python hands-on guide to understand the principles of generating new knowledge by following logical processes

15 November 2024 Bob Kasenchak, Factor As taxonomy consultants at Factor, we are frequently engaged to help our clients clean up and improve existing taxonomies; these have often developed ad hoc and, often, have not been constructed or managed by taxonomists. This is fine! Having controlled lists o

👉 Our paper "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-language…

Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.

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Showcase of Data Transformation Process, Breakdown of 25 dev hours involved, Schemas used, Questions & Responses, and Graph created