Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems

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

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 […]

Discover more about what's new at AWS with Amazon Neptune Analytics now supports openCypher queries over RDF Graphs

Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS.

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.

😎 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…

Using SPARQL and the Knowledge Graph for RAG

With Spanner Graph, you can analyze interconnected data using Google Cloud’s always-on, globally consistent, and virtually unlimited-scale database.

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 - Driving Business Outcomes with Connected Data

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

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

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 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

Learn how to build a technical support chatbot with deeper, more relevant retrieval.

Explore how using Neo4j Graph Database and Databricks together can deliver groundbreaking analytics and GenAI outcomes.

Describing graphs with GQL schemas

End to end solution for migrating CSV data into a Neo4j graph using an LLM for the data discovery and graph data modeling stages. - a-s-g93/neo4j-runway

A foray into how variables are defined and used.

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…

After 6 years of using GraphQL in production we aren't reaching for it as much as we once did. First up, security. GraphQL's self-documenting query API… | 161 comments on LinkedIn

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.

A short introduction what a GQL query looks like relative to Cypher

Neo4j Graph Data Science (GDS) now fully integrates with the Snowflake AI Data Cloud for advanced AI insights and predictive analytics.

PDF | The Digital Thread is a system that connects different phases of the product lifecycle and the related data across one or more companies in the... | Find, read and cite all the research you need on ResearchGate