How Semantic Partners converted the SFIA competency framework dataset to RDF, and made it available through the SFIA foundation.

This article demonstrates how developers or investigative journalists can use Senzing entity resolution (ER) to work with unstructured documents. In particular, given that ER has been used with structured data sources to construct a domain-specific KG, the results of ER can be leveraged to customise entity linking (EL) downstream, for example using spaCy — as an alternative to off-the-shelf EL sources such as DBPedia.

Empower enterprises to turn strategy into action! As a Knowledge Engineer at In Parallel, you'll craft ontologies and knowledge graphs to drive AI-powered strategic alignment and execution. Joi...

ICYMI, here are the slides from our standing room only talk at NeurIPS yesterday! Concepts we discuss include: ➡️ Quantifying how much Transformer you need to… | 18 comments on LinkedIn

Help us shape the future of RDF shape languages: share your insights Are you working with #RDF shape languages like #SHACL or #ShEx? Whether you’re a…

My colleague Su Zun and I spent a very enjoyable 3 days last week with the linked data community at Connected Data London. It was fantastic, and I'm so looking…

Showcase of Data Transformation Process, Breakdown of 20 dev hours involved, Schemas used, Questions & Responses, and Graph created

Following up on participation in the Connected Data London conference last week, my latest blog post is "Ontologies vs. Knowledge… | 13 comments on LinkedIn

GraphRAG is a powerful retrieval mechanism that improves Generative AI applications by taking advantage of the rich context in graph data structures.

🚀 R2R : The Most Advanced AI Retrieval System We're excited to announce R2R's V3 API, bringing production-ready RAG capabilities to teams building serious AI… | 10 comments on LinkedIn

An ontology is like a map of your organisation’s knowledge, built on the concepts and relationships that define your domain. Think of it as your company’s… | 20 comments on LinkedIn

A Conceptual Overview for Building Robust Solutions.

In this issue: A new standard for GraphRAG Replicating OpenAI’s strongest model LLM-”brained” agents for your devices Upgrade here 1. LazyGraphRAG: Setting a new standard for quality and cost Watching: LazyGraphRAG (blog) What problem does it solve? Retrieval Augmented Generation (RAG) has become a

SQL vs SPARQL: Two Titans of Data Querying 💻 When it comes to extracting valuable insights from data, SQL and SPARQL are often compared. But did you know…

Graphs + Transformers = the best of both worlds 🤝 The same models powering breakthroughs in natural language processing are now being adapted for graphs…

Our algorithm GNN: Graph Neural Network and Large Language Model Based for Data Discovery inherits the benefits of [Hoang(2024b)] (PLOD: Predictive Learning Opt

A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab. Of course I mean to say that Hicham Hallak won the… | 19 comments on LinkedIn

Agentic Graph Intelligence 🕳️ Agentic graph reasoning represents a sophisticated cognitive architecture that integrates multiple advanced components into a…

Enterprise GraphRAG: Building Production-Grade LLM Applications with Knowledge Graphs Let’s dive into the numbers: Real-World Results Implementing GraphRAG…

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…