— Sergey Dorogovtsev (@dorogovtsev)

End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages. - a-s-g93/neo4j-runway

In this post, we argue that the era of Graph FMs has already begun and provide a few examples of how one can use them already today.

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become...

Prof. Semih Salihoğlu discusses why Resource Description Framework (RDF) and the standards around it form a knowledge representation and reasoning (KRR) syst...

By request, here are the slides today from my keynote at the #CVPR workshop on scene graphs (SG2RL)! papers discussed: 1. Talk Like a Graph (ICLR'24) -… | 19 comments on LinkedIn

There is an increasing demand for knowledge graph engineers that start from semantic standards such as the Open Standard for Linking Organizations (#OSLO), the…

Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.

Graphs are all you need! Here is how graphs provide a way to understand and improve current deep learning architectures: It’s well known that graph neural… | 20 comments on LinkedIn

RAG + Knowledge Graphs cut customer support resolution time by 29.6%. 📉 A case study from LinkedIn. 🤝💼 Conventional RAG methods treat historical issue… | 10 comments on LinkedIn

A foray into how variables are defined and used.

OMG! 341 papers have been published on the topic of RAG (Retrieval Augmented Generation) since Jan 1, 2024: Naive RAG, Advanced RAG, GraphRAG … !  Please tell…

This notebook converts CSV data into a Neo4j Graph Database. All you do is describe your data. Have you wanted to see what your data looked like as a graph…

We had a very interesting session about QLever (SPARQL engine) by Hannah Bast at the Knowledge Graph Forum two weeks ago, and the demos in her slides were…

Graph RAG can perform much better than std RAG. Here’s when and how: When you want your LLM to understand the interconnection between your documents before…

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge and experience hallucinations during reasoning, which can...

LinkML is a language for modeling linked data

Following ICLR Graph Papers, I've created a repo for ICML graph papers, grouped by topic. We've got around 250 papers focusing on Graphs and GNNs in ICML'24.…

I spoke with Juan Sequeda about knowledge graphs and how he's leveraging them in the product at data.world - he also spoke about some of the new features tha...

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After 6 years of using GraphQL in production we aren't reaching for it as much as we once did. First up, security. GraphQL's self-documenting query API… | 161 comments on LinkedIn

Copyright 2024. Kurt Cagle / The Cagle Report A lot of the chatter lately in the machine learning community is beginning to shift towards discussions about semantics, and whether or not machines can actually "understand" them in any meaningful sense.

Excited to share that our recent work "Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting" is published at Scientific Reports…

Learn how graph and vector search systems can work together to improve retrieval-augmented generation (RAG) systems.

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...

Although they are often overlooked or downplayed, #taxonomies are critical for #DataManagement and #KnowledgeRepresentation, providing a structured framework… | 11 comments on LinkedIn

Wow, what a great farm-to-fork notebook by Jerry Liu that goes from 1) the exciting text of the San Francisco 2023 Budget Proposal (gnarly PDF!) all the way…

Using large language models to discern commonsense relationships can improve performance on downstream tasks by as much as 60%.

This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…