What are the key ontology standards you should have in mind? Ontology standards are crucial for knowledge representation and reasoning in AI and data… | 32 comments on LinkedIn

When (not) to use GraphRAG

Here is a usecase of an automated version of Sparnatural submitted as an example for Veronika Heimsbakk’s SHACL for the Practitioner upcoming book about the Shapes Constraint Language (SHACL). “ The Sparnatural knowledge graph explorer leverages SHACL specifications to drive a user interface (UI) that allows end users to easily discover the content of an RDF graph. What…

Explore its functionality and how it leverages the power of LLMs and Neo4j graph databases to improve data analysis and knowledge discovery.

Ontology is not only about data! Many people think that ontologies are only about data (information). But an information model provides only one perspective… | 85 comments on LinkedIn

Enterprise Ontology: A Human-Centric Approach to Understanding the Essence of Organisation : Dietz, Jan L. G., Mulder, Hans B. F.: Amazon.nl: Boeken

Knowledge graphs are rapidly gaining traction as a way to store and query complex, interlinked data. Two major approaches dominate this…

Enabling an LLM to chat directly with structured graph data

Why AI+Data Architecture Will Transform in 2025

David Hughes presented “Unleashing the Power of Multimodal GraphRAG: Integrating Image Features for Deeper Insights” at Data Day Texas 2025.

Terminology Augmented Generation (TAG)? Recently some fellow terminologists have proposed the new term "Terminology-Augmented Generation (TAG)" to refer to… | 29 comments on LinkedIn

What is really Graph RAG? Inspired by "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" paper from Microsoft! How do you combine… | 12 comments on LinkedIn

UChicago Genie is now open source! How we built a zero-hallucination AI chatbot that answered over 10000 questions of students at the University of… | 25 comments on LinkedIn

A few weeks ago, Thomas Francart asked me what we put in OWL, what we put in SHACL, and what our rule of thumb is to decide. I wrote this post to answer these…

Author: Nicolas Figay Status: DraftAuthor: Nicolas Figay Status: Draft Last update: 2025-01-14 This article was initiated due to the success of the following post A post being not enough for addressing the topic, here is the article developing the subject deeper. Introduction When diving into the wo

As technology advances, large language models (LLMs) and information retrieval/management methods evolve at breakneck speed. Knowledge…

Graph Retrieval Augmented Generation (Graph RAG) is emerging as a powerful addition to traditional vector search retrieval methods. Graphs are great at repre...

A RAG System Built for Both Semantic Search and Structured Data Queries

Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

While retrieval-augmented generation is effective for simpler queries, advanced reasoning questions require deeper connections between information that exist across documents. They require a knowledge graph.

Exploring OWL Ontologies Visually: A Paradigm Shift in Understanding 🌐 In the world of semantic web 🌐 and ontology modeling, inverse properties are a… | 24 comments on LinkedIn

🚀 Breaking Boundaries in Graph + Retrieval-Augmented Generation (RAG)! 🌐🤖 The rapid pace of innovation in combining graphs with RAG is absolutely…

We contributed recently to the "awesome semantic shapes" repository. This is a community-curated list of RDF shape resources, be it validators, generators…

With the enthusiasm for generative artificial intelligence (gen-AI) knowledge graphs are generating new interest. ‘Knowledge graph’ is a…

Semantic Partners put in action their SFIA RDF conversion: semantic use cases with SFIA, RDF graphs, vector search and LLMs

Lettria, an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, ...

Knowledge Graph community is divided into RDF adopters and Property Graph folks. It is the ultimate question for everybody who starts a…