GraphNews

🌟 TGB 2.0 @NeurIPS 2024 🌟 We are very happy to share that our paper TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs… | 11 comments on LinkedIn

Learn about the different kinds of data involved in developing, using and monitoring foundation AI models and systems.

Learn how to overcome them using knowledge graphs and structured tools

Publishers of Foundations and Trends, making research accessible

Don’t complicate things with graph DBs, QLs, or graph analytics.

Attention #Data and #Aalytics Leaders. Our team has fielded over 500 inquires on the subject of #DataProducts. In fact it is now one of the most popular topics… | 28 comments on LinkedIn

Data semantics is harder than rocket science! Hear Juan Sequeda argue why enterprises need to be investing in semantics. https://lnkd.in/gKV-Prk4 High…

More Graph, More Agents: Scaling Graph Reasoning with LLMs Graph reasoning tasks have proven to be a tough nut to crack for Large Language Models (LLMs).…

Unlocking universal reasoning across knowledge graphs. Knowledge graphs (KGs) are powerful tools for organizing and reasoning over vast amounts of… | 13 comments on LinkedIn

“Chat with a PDF” is so 2023. In 2024 we turn 1,000 PDFs into knowledge. In 2023, GenAI exploded and everyone had a side project to "chat with a PDF." That… | 22 comments on LinkedIn

Graph RAG answers the big questions where text embeddings won’t help you.

🚀 Exciting New Research: "Graph-constrained Reasoning (GCR)" - Enabling Faithful KG-grounded LLM Reasoning with Zero Hallucination! 🧠 🎉 Proud to share our… | 11 comments on LinkedIn

GraphRAG explained with an imaginary quiz game and not a single line of source code

Applications of Graph approaches in RAG — current state

A GCP native, fully serverless implementation that you will replicate in minutes

In this video, I introduce LightRAG, a new, cost-effective retrieval augmented generation (RAG) method that combines knowledge graphs and embedding-based ret...

⛔ The current challenge in building KGs from unstructured documents using LLMs is ensuring that the extracted triplets fully capture the provided context. 🟢…

Data Structures for Hyper-Personalized Lesson Plans

Document Metadata Generation, File Directory & Two-Step Information Retrieval Processes

Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.

Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that… | 19 comments on LinkedIn

“Ever wondered how to find or build Knowledge Graphs that power everything from recommendation engines to drug discovery?”

The 3 layers of Agentic Graph RAG 💬 The 3 layers of agentic graph RAG represent a significant advancement in AI-driven knowledge systems. These layers… | 17 comments on LinkedIn

Researchers created a knowledge graph based on 58 million journal papers to fuel personalized research ideas for scientists. Over 4400 ideas generated by the… | 29 comments on LinkedIn

Hello wonderful people, I’ve noticed some common misconceptions about graphs and AI circulating on LinkedIn, and I thought it might be helpful to share some insights to clarify these topics. I hope…

Glean | Glean’s knowledge graph and signals enables it to provide state-of-the-art hybrid search and generative AI that only gets better over time.

In this series of posts I make an appeal to organize global financial institutions and RiskTech vendors to collaborate on an open-source repository of financial risk patterns called network motifs to…