Introducing the Fine-Tuned Neo4j Text2Cypher (2024) Model
Authors: Makbule Gulcin Ozsoy, Leila Messallem, Jon Besga
Introduction to the graph technology landscape
The world of graph technology is vast and evolving. We're here to help you make sense of the key trends and players.
Building Knowledge Graphs with LLM Graph Transformer
A deep dive into LangChain’s implementation of graph construction with LLMs
Announcing New Aura Import Services
PostgreSQL to Graph querying in less than 5 minutes
Unum · UStore 0.13.12 documentation
now publishers - Modern Techniques For Querying Graph-structured Databases
Publishers of Foundations and Trends, making research accessible
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.
What is the path in this supply chain that leads to the least CO2 emissions?
If you care about sustainability, I have a data challenge to pull your hair out. What is the path in this supply chain that leads to the least CO2 emissions?…
What is the path in this supply chain that leads to the least CO2 emissions?
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! 🚀
The Fastest Graph Database in the World?
We have some pretty exciting news! We have filed an international patent on our new graph database query algorithm. We have also done some further benchmarking tests, and the results are pretty astounding.
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
Graph patterns ➤ Projecting subgraphs | LinkedIn
LDBC TUC: a focus on graph data in China Shanghai -- We’ve recently come out of two long, interesting days at LDBC’s 18th Technical Users Committee meeting in Guangzhou, in southern China. This post largely concentrates on one point that came up twice at the meeting: how to define subgraphs to be ex
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
LIquid: a Large-Scale Relational Graph Database
Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.
Triple your knowledge graph speed with RDF linked data and openCypher using Amazon Neptune Analytics | Amazon Web Services
There are numerous publicly available Resource Description Framework (RDF) datasets that cover a wide range of fields, including geography, life sciences, cultural heritage, and government data. Many of these public datasets can be linked together by loading them into an RDF-compatible database. In this post, we demonstrate how to build knowledge graphs with RDF linked data and openCypher using Amazon Neptune Analytics.
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
Announcing Spanner Graph | Google Cloud Blog
With Spanner Graph, you can analyze interconnected data using Google Cloud’s always-on, globally consistent, and virtually unlimited-scale database.
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.
Graph-Powered Analytics And Machine Learning With TigerGraph
Graph-Powered Analytics And Machine Learning With TigerGraph - Driving Business Outcomes with Connected Data
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
A Brief History of SQL and the Rise of Graph Queries
From SQL to GQL, the new graph query language standard and a pivotal development for advancing data analytics in the era of GenAI and beyond.
(PDF) BIFROST: A Future Graph Database Runtime
PDF | BIFROST is a novel query engine for graph databases that supports high-fidelity data modeling on arbitrary and evolving graph topologies. It... | Find, read and cite all the research you need on ResearchGate
Whatgoesaround sigmodrec2024
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
Neo4j-Databricks Connector Delivers Deeper Insights, Faster GenAI Development - Graph Database & Analytics
Explore how using Neo4j Graph Database and Databricks together can deliver groundbreaking analytics and GenAI outcomes.
GQL: Schemas and Types
Describing graphs with GQL schemas