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…

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...

🚀 What happens when LLMs meet Graphs? 🔍 Excited to share our new [#KDD'2024] Survey+Tutorial on 🌟LLM4Graph🌟: "A Survey of Large Language Models for…

🚀 When #Transformers meet Graph Neural Networks (#GNNs)! Introducing our 🌟GFormer🌟: A new paradigm for Recommender Systems. 📄 [#SIGIR2023] 🔥Graph…

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 /ul 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.

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become...

Prof. Semih Salihoğlu discusses why Resource Description Framework (RDF) and the standards around it form a knowledge representation and reasoning (KRR) syst...

There is an increasing demand for knowledge graph engineers that start from semantic standards such as the Open Standard for Linking Organizations (#OSLO), the…

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…

We had a very interesting session about QLever (SPARQL engine) by Hannah Bast at the Knowledge Graph Forum two weeks ago, and the demos in her slides were…

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...

LinkML is a language for modeling linked data

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...

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Copyright 2024. Kurt Cagle / The Cagle Report A lot of the chatter lately in the machine learning community is beginning to shift towards discussions about semantics, and whether or not machines can actually "understand" them in any meaningful sense.

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

Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One critical challenge...

Although they are often overlooked or downplayed, #taxonomies are critical for #DataManagement and #KnowledgeRepresentation, providing a structured framework… | 11 comments on LinkedIn

Wow, what a great farm-to-fork notebook by Jerry Liu that goes from 1) the exciting text of the San Francisco 2023 Budget Proposal (gnarly PDF!) all the way…

Using large language models to discern commonsense relationships can improve performance on downstream tasks by as much as 60%.

This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…

How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models

At Semantic Partners, we wanted to build our informed opinion over the strengths and weaknesses of graph RAG for RDF triple stores. We considered a simple use case: matching a job opening with Curriculum Vitae. We show how we used Ontotext GraphDB to build a simple graph RAG retriever using open, offline LLM models – the graph acting like a domain expert for improving search accuracy.