GraphRAG: New tool for complex data discovery now on GitHub
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:
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...
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...
“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...
How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks
💡 How to develop a Graph Foundation Model (GFM) that benefits from large-scale training with better generalization across different domains and tasks? 🔎…
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
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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.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
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DiffKG: Knowledge Graph Diffusion Model for Recommendation
GraphGeeks Talk Ep6: RDF vs Property Graphs: Ask Me Anything
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...
GitHub - a-s-g93/neo4j-runway: End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages.
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
A framework for developing a knowledge management platform
Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become...
RDF - Part 2: RDF as knowledge representation and reasoning system
Prof. Semih Salihoğlu discusses why Resource Description Framework (RDF) and the standards around it form a knowledge representation and reasoning (KRR) syst...
Giving a Voice to Your Graph: Representing Structured Data for LLMs
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…
GraphStorm: all-in-one graph machine learning framework for industry applications
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 provide a way to understand and improve current deep learning architectures
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%
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
