GraphNews

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…

We are excited to announce the Beta launch of the WhyHow.AI Knowledge Graph Studio Platform, the easiest way to build KG RAG systems.

Safety RAG (SRAG) is how we get to 100% hallucination-free answers.

RAG is popular but unfit for many enterprise use cases and I explain both of those things.

Utilizing knowledge graphs is one popular solution to drive up the performance of AI applications. We work closely together with other key players such as Emil…

An open-source model for 10x cheaper knowledge graph construction

In the past year or so, our research team designed, developed and published RDFGraphGen, a general-purpose, domain-independent generator of synthetic RDF…

Large Language Models (LLMs) have demonstrated unprecedented prowess across various natural language processing tasks in various application domains. Recent studies show that LLMs can be leveraged...

An easy trick to improve your LLM results without fine-tuning. Many people know "Few-Shot prompting" or "Chain of Thought prompting". A new (better) method was… | 77 comments on LinkedIn

Using knowledge graphs to improve the performance of RAG systems by optimising retrieval design architectures and collaborative filtering.

Knowledge Graph Embedding (KGE) algorithms can improve the performance of LLMs in understanding relationships as a context for Q&As.

Learn how to use unstructured.io for PDF document parsing, extracting, and ingestion into the Neo4j graph database for GenAI applications.

Let’s talk about the new graph-based approach to processing complex datasets for context-rich LLM responses

Have you wondered about graph theory and how to start exploring it in Python? What resources and Python libraries can you use to experiment and learn more? This week on the show, former co-host David Amos returns to talk about what he's been up to and share his knowledge about graph theory in Python.

RAG-Native Graph Creation have several misconceptions from traditional graph creation. We look to tackle some of these misconceptions.

Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures...

𝗥𝗔𝗚 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗶𝗹 𝗗𝘂𝗲 𝗧𝗼 𝗜𝗻𝘀𝘂𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗙𝗼𝗰𝘂𝘀 𝗢𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗻𝘁 𝘛𝘩𝘦 𝘔𝘪𝘯𝘥𝘧𝘶𝘭-𝘙𝘈𝘎… | 12 comments on LinkedIn

Additionally, there are evaluation methods where LLMs serve as the evaluators, including single point, reference-based, and pairwise-based…

Get rid of hallucination's and inconsistencies.

Graph-Powered Analytics And Machine Learning With TigerGraph - Driving Business Outcomes with Connected Data

Knowledge graphs deliver semantically enabled data management to power a diverse range of AI applications. To successfully build knowledge graphs at an enterprise level, data and analytics leaders must take an agile approach to knowledge graph development.

The Advantages of Small Graphs for Construction, Retrieval, Maintenance and Scaling KG RAG systems.

When we progress from data to knowledge, there is what physicists call a phase change like the change from water to ice or from mud to brick. The ingredients are the same throughout the transition, but we compress and restructure these ingredients into something entirely new with dramatically differ

Ten Arguments

🥳 The Wait is Over! As promised from my last post (https://lnkd.in/g9_-9i8D), I took MSFT open-source GraphRAG for a 🏎️💨 road test via my JAM4RAG (Just… | 12 comments on LinkedIn

Many people have asked about loading Microsoft Research #GraphRAG data into Neo4j. I wrote a quick notebook last night to import Documents, Chunks (TextUnit)… | 27 comments on LinkedIn

Examples of KG + RAG & Schemas that are used by various systems across different domains, for inspirational purposes.

An Overview of Knowledge Graph Embeddings (KGEs) – Part 1. 🧠🍄 Knowledge Graphs represent real-world facts as structured data. Nodes represent entities or…