GraphNews

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

This study, sponsored by Graphwise, explores LLMs and RAG in enterprises and provides statistics from executives across various industries.

Nicola Guarino Keynote Address for the Ontology Summit 2025 on 22 January 2025 "Ontologies as specifications of conceptualizations: correctness, precision, a...

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

G.V() 3.14.38 Release Notes: Now with Support for Neo4j, Memgraph, Neptune Analytics, Query Editor Improvements, and more! For the first time ever, G.V() can be used on non Apache TinkerPop graph databases. It is now compatible with Neo4j, Neo4j AuraDB, Memgraph and Amazon Neptune Analytics using the Cypher querying language.

RDFox® is the technology behind Samsung’s Personal Data Engine to create hyper-personalised user experiences, while ensuring privacy and security of data on the device.

🚀 Knowledge graphs are shaping the future of data and AI, and I’m excited to see them featured in the Data Gang’s predictions for 2025! 🚀 Every year I enjoy… | 10 comments on LinkedIn

Keith W. Hare, Convenor, ISO/IEC JTC1 SC32 WG3 Database Languages The GQL standard (ISO/IEC 39075:2024 Information technology — Database languages — GQL) was published in April, 2024.

Discover Neo4j's 2024 product innovations in cloud-native graph technology, enhancing performance, scalability, AI innovation, and developer productivity.

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

Mapping Workbench transforms XML data into harmonized RDF using Precise Mapping. This is a collaborative tool used by semantic engineers to efficiently map…

Knowledge Graphs as a source of trust for LLM-powered enterprise question answering That has been our position from the beginning when we started our research… | 29 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

Graph contrastive learning (GCL) is a self-supervised learning technique for graphs that focuses on learning representations by contrasting different views of…

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…

The SEMIC Style Guide for Semantic Engineers provides guidelines for developing and reusing semantic data specifications, particularly eGovernment Core…

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

This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies....

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.

Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is...

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

What are the key components of an ontology? Ontologies can seem a bit abstract at first, but when you break them down into their core components, they become… | 21 comments on LinkedIn

Create Cypher queries with an easy-to-use drag-and-drop interface

Stop struggling with Cypher syntax Turn graph queries into drag-and-drop Moving from SQL to Cypher presents a common challenge. You understand how data… | 54 comments on LinkedIn