Information Architecture

Most AI systems get answers wrong because they lack context and cannot reliably connect the dots between data points. Structuring relationships between data enables AI to reason accurately and produce reliable, production-ready answers.

Written for organizations that already have systems, already have data, and are still asking why none of it feels reliable.

Why building for machine readability makes things better for the humans in your operation too - and what that means for how you should approach it

Why the shift to machine readability is a meaning problem, not a format problem - and what it demands from your organization.

New data visualisations in Data Graphs lets you view your domain model and explore your knowledge graph visually.

Introducing Data Graphs - a fully managed SaaS structured data solution, that combines an intuitive user experience with flexible integration.

Guest blogger Jim Howard shares his experiences in Sports publishing patterns, and how to extract most value from your sports data.

How Cochrane are marking up COVID-19 clinical studies with Schema.org and PICO structured data.

A knowledge graph case study on the data architecture of the Cochrane COVID-19 Study Register and the Cochrane Linked Data Project.

A discussion on the synergies between GraphQL and Graph Databases, data architecture and how ontology models provide the special ingredients.

A discussion of AI and Data Science delivery anti-patterns, their impacts on data platform technical architectures, and strategies for reducing complexity and mitigation.

An introductory guide to tagging, Part 1. Examining how developments in search technology created the perception that good information management practices were redundant.