See how to combine structured and unstructured semantic queries and to use large language models to orchestrate question answering over a knowledge graph.

Recently, Microsoft announced with a post their GraphRAG offering. This article provides a brief overview of their approach, how it compares to other Graph RAG varieties, what problems it can address and what it cannot.

Knowledge graphs (KGs) are a specific type of #data structure designed to represent entities and the connections between them. They move beyond simply storing… | 14 comments on LinkedIn

Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy

The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI

Ask your (research) question against 76 Million scientific articles: https://ask.orkg.org Open Research Knowledge Graph (ORKG) ASK (Assistant for Scientific…

Copyright 2024 Kurt Cagle / The Cagle Report Recently, I've spent a lot of time talking with clients about the need for knowledge graphs in LLMs, why they are not "graphlike," and why we may need to rethink the whole transformer model. I think this topic is worth exploring, and I'd like to have a po

Predicting the architecture of intelligent organizations

Our paper "Are Large Language Models a Good Replacement of Taxonomies?" was just accepted to VLDB'2024! This finished our last stroke of study on how knowledgeable LLMs are and confirmed our recommendation for the next generation of KGs. How knowledgeable are LLMs? 1.

Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…

What Could Go Wrong When We Start Using LLMs to Organize Knowledge? 7 Pain Points of GraphRAG Alright, tech enthusiasts and AI aficionados. We need to discuss… | 43 comments on LinkedIn

Generalist and Lightweight Model for Relation Extraction (Extract any relationship types from text) - jackboyla/GLiREL

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

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.

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...

💡 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…

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

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

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…

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...

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...

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