Building Knowledge Graphs with LLM Graph Transformer
A deep dive into LangChain’s implementation of graph construction with LLMs
Turn Your CSVs Into Graphs Using LLMs
Learn how to turn CSV files into graph models using LLMs, simplifying data relationships, enhancing insights, and optimizing workflows.
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe
Awesome Graph Universe 🌐
Welcome to Awesome Graph Universe, a curated list of resources, tools, libraries, and applications for working with graphs and networks. This repository covers everything from Graph Databases and Knowledge Graphs to Graph Analytics, Graph Computing, and beyond.
Graphs and networks are essential in fields like data science, knowledge representation, machine learning, and computational biology. Our goal is to provide a comprehensive resource that helps researchers, developers, and enthusiasts explore and utilize graph-based technologies.
Feel free to contribute by submitting pull requests! 🚀
Vendors offering intelligent document processing, graph technologies (knowledge graphs and graph databases) for GraphRAG and LLM fine tuning, enterprise retrieval, and services surrounding these technologies, will be best positioned for this new wave of data and metadata management needs
💡 The relevance, trustworthiness and quality of AI and #GenAI applications is increasingly dependent on the quality of enterprise private data and documents…
Vendors offering hashtag#intelligentdocumentprocessing, hashtag#graphtechnologies (hashtag#knowledgegraphs and hashtag#graphdatabases) for hashtag#GraphRAG and hashtag#LLMfinetuning, hashtag#enterpriseretrieval, and services surrounding these technologies, will be best positioned for this new wave of data and metadata management needs
Implementing GraphReader with Neo4j and LangGraph
Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems
Build and deploy knowledge graphs faster with RDF and openCypher | Amazon Web Services
Amazon Neptune Analytics now supports openCypher queries over RDF graphs. When you build an application that uses a graph database such as Amazon Neptune, you’re typically faced with a technology choice at the start: There are two different types of graphs, Resource Description Framework (RDF) graphs and labeled property graphs (LPGs), and your choice of […]
Amazon Neptune Analytics now supports openCypher queries over RDF Graphs - AWS
Discover more about what's new at AWS with Amazon Neptune Analytics now supports openCypher queries over RDF Graphs
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
😎 Pretty proud to see Ontotext mentioned in more than 10 Gartner Hype Cycle reports over the last couple months! This demonstrates the success of our…
Ontotext mentioned in more than 10 Gartner Hype Cycle reports
Microsoft GraphRAG with an RDF Knowledge Graph — Part 3
Using SPARQL and the Knowledge Graph for RAG
Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune | Amazon Web Services
Retrieval Augmented Generation (RAG) is an innovative approach that combines the power of large language models with external knowledge sources, enabling more accurate and informative generation of content. Using knowledge graphs as sources for RAG (GraphRAG) yields numerous advantages. These knowledge bases encapsulate a vast wealth of curated and interconnected information, enabling the generation of responses that are grounded in factual knowledge. In this post, we show you how to build GraphRAG applications using Amazon Bedrock and Amazon Neptune with LlamaIndex framework.
loading Microsoft Research GraphRAG data into Neo4j
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
loading Microsoft Research hashtag#GraphRAG data into Neo4j
GitHub - SynaLinks/HybridAGI: The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
The Programmable Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected - SynaLinks/HybridAGI
Better LLM Integration With Content-Centric Knowledge Graphs
Learn how to build a technical support chatbot with deeper, more relevant retrieval.
Content-Centric Knowledge Graphs
This notebook converts CSV data into a Neo4j Graph Database
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…
Extended SemanticWeb Conference trip report
This week, I thoroughly enjoyed attending the 21st Extended #SemanticWeb Conference! Here’s a summary of the contributions presented at the conference about…
Extended hashtag#SemanticWeb Conference
A Triple Store RAG Retriever
How SP used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models
A Triple Store RAG Retriever
How we used Ontotext GraphDB’s vector capabilities to build a simple RAG retriever over a Knowledge Graph, using open, offline LLM models
Matching skills and candidates with Graph RAG
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.
Schema language for both RDF and LPGs
Graph Data Models for RAG Applications
When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.
Why I Chose FalkorDB Knowledge Graph Over Neo4j for Building a RAG Application
A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.
The Rise of Graph Jobs, The Disappearance of Graph Technology?
The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…
A few thoughts on RDF-star, Reification, and Labeled Property Graphs
The W3C RDF-star WG is currently debating a design issue which may greatly affect our chances of having a better alignment between RDF graphs and Labeled… | 13 comments on LinkedIn
LLMs, Knowledge Graphs and Property Graphs
In response to my last blog, LLMs and Knowledge — the study continues, I got a lot of questions LinkedIn, which I decided to answer in…
Introduction to Graph query language Cypher
Sorry Telcos, graph databases are no way to run a business
Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them
GenAI Ecosystem - Neo4j Labs
Knowledge Graph Construction with Ontologies in Neo4j— Part 2
Continuing our exploration of Knowledge Graphs and Ontologies, this next part delves into hands-on implementation. Let’s transform flat CSV…
Implementing Advanced Retrieval RAG Strategies With Neo4j
Go beyond typical RAG strategies
Knowledge Graphs: use the right tool for the right job
This is the first of a series of blogs wherein I describe what we believe is a superior pattern for creating highly performant, real-world…