GraphNews

It was recorded and I hope it'll get… — Emil Eifrem (@emileifrem)

Every time I write about why graph-based RAG produces more insightful and more accurate answers for Q&A / digital assistant AI applications, people ask — do… | 21 comments on LinkedIn

If you, like me, aspire to create your knowledge graph on Google, I have two recommendations for you: 1º Use the tool at demo.nl.diffbot.com to visualize the…

GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub. Learn more:

Learn how to build a technical support chatbot with deeper, more relevant retrieval.

Explore how using Neo4j Graph Database and Databricks together can deliver groundbreaking analytics and GenAI outcomes.

Associating unstructured data with structured information is crucial for real-world tasks that require relevance search. However, existing graph learning benchmarks often overlook the rich...

Describing graphs with GQL schemas

The Zazuko Knowledge Graph Forum serves as a platform where companies are invited to share their ongoing work and use cases with Knowledge Graphs. Our goal i...

Neo4j’s Philip Rathle on the Rise of GraphRAG and GQL.

“I knew I shoulda’ taken that left turn at Albuquerque.” – Bugs Bunny For better or worse, much of my childhood was informed by Looney Tunes, Monty Python, and a diet of science fiction rangi…

GraphReader: Long-Context Processing in AI ... As AI systems tackle increasingly complex tasks, the ability to effectively process and reason over long…

Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno are trained on a huge amount of language corpus, images, videos, and audio that are extremely diverse from numerous...

🚀 What happens when LLMs meet Graphs? 🔍 Excited to share our new [#KDD'2024] Survey+Tutorial on 🌟LLM4Graph🌟: "A Survey of Large Language Models for…

💡 How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks? 🔎…

🚀 When #Transformers meet Graph Neural Networks (#GNNs)! Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems. 📄 [#SIGIR2023] 🔥Graph…

Upgrade your RAG applications with the power of knowledge graphs./b Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM’s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Knowledge Graph-Enhanced RAG/i shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Knowledge Graph-Enhanced RAG/i you’ll learn: The benefits of using Knowledge Graphs in a RAG system/li How to implement a GraphRAG system from scratch/li The process of building a fully working production RAG system/li Constructing knowledge graphs using LLMs/li Evaluating performance of a RAG pipeline/li /ul Knowledge Graph-Enhanced RAG/i is a practical guide to empowering LLMs with RAG. You’ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, and generate Cypher statements to retrieve data from a knowledge graph.

This Large Graph Model (LGM) has undergone training on a diverse set of 5,000 graphs across 13 different domains. It serves as a valuable tool for…

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Graphs are changing the way we model, store, and query complex data. But when it comes to choosing the right type of graph model, the decision often boils do...

🚀 Enhance your graph analytics with 🔥GraphEdit 🔥 - the innovative Large Language Model (#LLM) tailored for graph structure learning and editing! 🌟…

— 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…