GraphNews

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.

Zazuko Blueprint is an enterprise knowledge graph frontend and browser, designed to make RDF Knowledge Graphs accessible and customizable for domain users. - zazuko/blueprint

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

From SQL to GQL, the new graph query language standard and a pivotal development for advancing data analytics in the era of GenAI and beyond.

PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate

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

Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies.

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...