When I started building knowledge graph systems for healthcare and enterprise data, I quickly realized there's no single reference showing which tools actually work together. You have document extraction libraries, graph databases, visualization tools, and LLM integrations—but which ones belong in your stack? That's why I've created a comprehensive guide to 100+ graph libraries, organized… Continue reading Useful Graph Libraries Guide
Turning unstructured data into a graph database is a deceptively complex problem. You need to extract entities, understand relationships, define schemas, and maintain data quality whilst managing costs and supporting new data sources. The challenge isn't choosing whether to use a graph database; it's choosing how to populate it. This guide walks through seven different… Continue reading A Complete Guide to Data Ingestion Approaches from Documents to a Graph Database
Unstructured and Unlinked Policy Documents ACME_CORP has policies scattered everywhere: PDF files in shared drives, API responses from regulatory systems, manually created requirement documents, compliance frameworks from vendors. Each source is different. Each format is inconsistent. And yet, your auditors want to know: Which rules actually prevent unauthorized access? Where do we have conflicting compliance… Continue reading Building Intent-Based Policy Graphs with Neo4j
Relational databases handle type hierarchies badly. A common workaround is a "type" column in a table, which flattens all subtypes into a single wide row and forces application code to pick which columns apply. Object-oriented code has classes, inheritance but Java struggles with multiple parents. Graph databases have something more profound. The relationship itself carries… Continue reading Inheritance in Graph Databases: How to Model a Type and Its Subtypes
Graph databases promise a natural fit for complex, relationship-heavy domains. But there's a successful implementation gap between a promising prototype and a production system that performs, scales, and survives organisational change. This article is based on my personal experience building three real-world graph ontologies in three different organisations. First was BT's network inventory (SRIMS), second… Continue reading Graph Ontologies Design from BT, Nature Metrics & NHS England
Some medical queries are too complex for relational databases and data warehouses. A graph database is the right tool for mapping data coded to different libraries and canonical models. Take as a query: "Find every patient with a diabetes subtype who is on an ACE inhibitor and has stage 3 or worse kidney disease, where… Continue reading Graph Databases for Primary Care handling complex ontologies with SNOMED on Neo4J
When you're operating at the scale of NHS England, the question of which tools you use to understand and protect your digital estate isn't academic. It's existential! The Scale Problem NHS England is not one organisation. It has 7,900+ unique procuring organisations including acute trusts, integrated care boards, mental health providers, community services, GP federations,… Continue reading When Do You Actually Need a Cyber Asset Attack Surface Management Product
Do you know your Zeno of Citium from your Camus and are you more of a Stoic than an Absurdist?
