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Pushing AI On-Device with GraphRAG 🚀. Contribute to trustgraph-ai/trustgraph development by creating an account on GitHub.

#Alhamdulillah, Our iText2KG has achieved over 300 stars and 27 forks in just 10 days after its release, and it is currently ranked among the top 12 trending…

Your first ontology doesn’t need to be an ontology. Here’s what I mean. Combining ontologies with knowledge graphs is a powerful approach. But implementing…

Web Ontology Language (OWL) emerges as a superior choice for knowledge representation in generative AI, offering unparalleled expressiveness, reasoning capabilities, and semantic richness. By leveraging OWL-based knowledge graphs, AI systems can generate more accurate, context-aware, and nuanced outputs across diverse ...

As the volume of digital content increases, the ability to manage it becomes more important. Taxonomy and metadata are vital to finding products, conducting scientific research, and keeping track of organizational information. They also enable a wide variety of analytics on unstructured data. We can see the results of a well-designed taxonomy, but behind the scenes, there's a lot more going on than meets the eye.

My presentation at the "First International Workshop on Scaling Knowledge Graphs for Industry" at the SEMANTICS 2024 Conference in Amsterdam was focused on the…

DOM Graph RAG: A semantic content architecture for neuro-symbolic RAG

Introduction

i want to share the actual steps we’re using to generate text to sql through an ontology instead of an LLM [explained with a library analogy]: 𝟭… | 15 comments on LinkedIn

Covering the advanced RAG options in R2R

The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often...

✨ Operationalizing the information architecture 👇 There are three main ways to operationalize the information architecture, depending on how the data plane… | 14 comments on LinkedIn

A Step-by-Step Tutorial on Implementing Retrieval-Augmented Generation (RAG), Semantic Search, and Recommendations

"From Semiotic to Digital Enterprise Semantic Interoperability? Translation in english with some completion of my last article in French. Enterprises are…

GraphRAG uses LLM-generated knowledge graphs to substantially improve complex Q&A over retrieval-augmented generation (RAG). Discover automatic tuning of GraphRAG for new datasets, making it more accurate and relevant.

Have you ever entered a news-spelunking time-machine 🧗? Well AskNews built one...and our users are already jumping into the time-machine to explore the…

An example of the application of #LegalKit is the production of knowledge #graphs, here is a Hugging Face demo #Space 🤗 With the update of the French legal…

Where do you start when you want to build an ontology? Building an ontology sounds like a big, complex task, right? With all those high-level frameworks like… | 28 comments on LinkedIn

Establishing this open data portal to share our reference data and schema with customers, stakeholders, and partners under a permissive open data license.

I keep seeing mentions of "entity-resolved knowledge graphs", which leads me to believe that other so-called "knowledge" graphs don't resolve their entities. But a knowledge graph without entity resolution is like a beach under deep snow and dark skies.

Hype Cycle for Data Management, 2024

How can we represent change in complex systems using Ontologies? In complex systems, understanding and representing change over time can be challenging. Basic… | 52 comments on LinkedIn

A step-by-step guide using Python

This is "Knowledge Graphs for Dummies! - Veronika Heimsbakk" by JavaZone on Vimeo, the home for high quality videos and the people who love them.

This article was based on Transmute Solutions Architect Nis Jespersen’s ‘UN/CEFACT Linked Data’ presentation from December 2022.

Knowledge Graph technologies are promising to implement in Business Intelligence Systems due to encoding different relations between…

LDBC TUC: a focus on graph data in China Shanghai -- We’ve recently come out of two long, interesting days at LDBC’s 18th Technical Users Committee meeting in Guangzhou, in southern China. This post largely concentrates on one point that came up twice at the meeting: how to define subgraphs to be ex