My takeaways from the International Semantic Web Conference #iswc2024 Ioana keynote: Great example of data integration for journalism, highlighting the use of…

I'm coming around to the idea of ontologies. My experience with entity extraction with LLMs has been inconsistent at best. Even running the same request with… | 63 comments on LinkedIn

Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga

Beyond Vector Space : Knowledge Graphs and the New Frontier of Agentic System Accuracy ⛳ In the realm of agentic systems, a fundamental challenge emerges…

👉 Our paper "Understanding SPARQL Queries: Are We Already There?", explores the potential of Large Language Models (#LLMs) to generate natural-language…

Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.

Showcase of Data Transformation Process, Breakdown of 25 dev hours involved, Schemas used, Questions & Responses, and Graph created

Introduction

I spent the day in San Francisco yesterday attending part 2 of Neo4j's GenAI Graph Gathering. Along with the original session back in May, this was one of the…

A deep dive into LangChain’s implementation of graph construction with LLMs

While PuppyGraph is less than a year old, it is already witnessing success with several enterprises, including Coinbase, Clarivate, Dawn Capital and Prevelant AI.

Large Language Models (LLMs) are revolutionizing AI capabilities, but organizations face challenges in reducing inaccuracies and protecting valuable data. Knowledge Graphs offer a solution, helping improve LLM accuracy and safeguard organizational data assets. Learn how implementing Knowledge Graphs can address these critical issues and maintain competitiveness in the AI landscape.

A question-based extraction approach

We are launching a series of benchmarks to prove the value of GraphRAG for question & answering on complex documents. The process is simple, we ingest the…

Language agents have recently been used to simulate human behavior and user-item interactions for recommendation systems. However, current language agent simulations do not understand the...

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

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

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

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

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

After a period of more than a year (can't believe time flew by so quick!), I had the pleasure of going back for a second time on the Practical AI Podcast with…

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