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.

When to use graph data models, such as parent-child, question-based, and topic-summary, for RAG applications powered by knowledge graphs.

A comparison of FalkorDB with other Knowledge Graphs for developing AI applications with.

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…

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

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…

Semantic Web and Labeled Property Graphs have their use-cases, but complex, real-time business operations is not one of them

Continuing our exploration of Knowledge Graphs and Ontologies, this next part delves into hands-on implementation. Let’s transform flat CSV…

Go beyond typical RAG strategies

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…

Reference Architectures for Graph Databases on AWS - aws-samples/aws-dbs-refarch-graph

Natural language processing made easy

Building enterprise knowledge graphs as property graphs of billions of nodes / edges holding organizational knowledge to power your…

Seamlessy implement information extraction pipeline with LangChain and Neo4j

In the era of information overload, sifting through vast amounts of data to provide accurate search results in an engaging and…

Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain, which uses LLMs to generate Cypher statements. This…

Tired of updating your #scraping templates or feeding full webpages to LLMs? Use #Cypher instead! I was in the same boat just 3 months ago when I was working… | 16 comments on LinkedIn

See how to integrate a language language model with TigerGraph to empower question answering with your connected data.

📢 📢 📢 Amazing milestone! 📢 📢 📢 Neosemantics (n10s) reaches the first million all-time downloads 🤯 Let's keep building Knowledge Graphs together! 💪… | 21 comments on LinkedIn

What exactly are knowledge graphs, and what's with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.

Knowledge graphs are among the most important technologies for the 2020s. Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages

A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

Data lakes can be a great asset, but they need an array of elements to work properly. We take a look at how it works for Montefiore Health System and discuss the role of semantics and graph databases in the data lake architecture.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

Graph databases are making a splash in the database market, with specialist, multimodal and cloud database suppliers jostling for a slice of the pie.