What does graph have to do with machine learning, and data science? A lot, actually, and it goes both ways.

Building a biomedical knowledge graph using publicly available datasets to better aid disease research and biomedical data modelling.

This article summarizes the problem statement, solution, and other key technical components of the paper: End-to-End Neural Entity Linking in JP Morgan Chase

What does it take to be an obsessive notetaker? Some say it’s talent, others a curse. I say it's the only way to live — let me show you.

Foursquare is evolving, and its next steps will be powered by the Foursquare Graph

Writing a book is like a hero's journey. Writing the first Personal Knowledge Graphs book involved more than one hero and a few crises.

News and analysis on where Knowledge Graphs, Graph Databases, Graph Analytics and Graph AI are today and where they’re headed to. New types of Knowledge Graphs.

Amazon Neptune, the managed graph database service by AWS, makes analytics faster and more agile while introducing a vision aiming to simplify graph databases.

Graphs, analytics and Generative AI. An account of the different ways graphs and AI mingle, plus industry and research news.

What is a skills-based economy and how is LinkedIn moving from vision to implementation? There’s AI, taxonomy and ontology involved in building the Skills Graph

This post explores the use of knowledge graphs for RAG, using DataStax Astra DB for storage. The code for the examples is in this notebook using some prototype

What is a universal semantic layer, and how is it different from a semantic layer? Is there actual semantics involved? Who uses that, how, and what for?

What is Graph RAG, what can it do, and how do you evaluate it?

Learn how to use GenAI, LLMs, and Python to convert unstructured data into graphs

Explore a practical guide for constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain.

Use Llama 3.1 native function-calling capabilities to retrieve structured data from a knowledge graph to power your RAG applications.

Combine text extraction, network analysis, and LLM prompting and summarization for improved RAG accuracy.

Discover the limits of text embeddings in RAG applications and explore how knowledge graphs handle structured data for accurate information retrieval.

Build a Graph-RAG agent using Neo4j and Milvus to combine graph and vector search, delivering accurate, context-rich answers to complex queries.