Sponsored Post: Volumes of data being generated across the earth are growing at seemingly exponential rates: Statista predicts that the total amount of data created, captured, copied, and consumed globally is now on track to rack up an eye-watering 180 zettabytes by 2025.
Avoiding drowning in this ever-increasing data deluge is a serious challenge, but not an insurmountable one, according to a new book, Building Knowledge Graphs: A Practitioner’s Guide, published by our good friends O’Reilly. The tome’s authors, Jesús Barrasa and Dr. Jim Webber, argue that “all is not lost” because a new category of technology, based on graphs, can help extract real value from what would otherwise be an unmanageable data tsunami.
Wikipedia defines a “knowledge graph” as a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs can be very handy for the storage and representation of interconnected descriptions of a wide variety of things, including objects, events, situations, or abstract concepts.
In their new book Barrasa and Webber explain that knowledge graphs can underpin everything from consumer-facing systems like navigation and social networks to critical infrastructure like supply chains and power grids.
“We wrote this book for technology professionals who are interested in building and operating knowledge graphs within their businesses,” the authors say.
They go on to point out that knowledge graphs are valuable because they can provide contextualised understanding of data. Context derives from the layer of metadata (graph topology and other features) that provides rules for structure and interpretation. The book shows how when connected, that context enables data pros to extract greater value from existing data, drive automation and process optimisation, improve predictions, and support an agile response to changing business environments.
Building Knowledge Graphs, a Practitioner’s Guide has been written specifically to meet the needs of data and software professionals who assemble sophisticated information systems. It is split into two sections: the first part deals with graph fundamentals, including graph databases, query languages, data wrangling, and graph data science. This initial section is designed to get data specialists up to speed with the fundamentals. The second half of the book tackles “significant knowledge graph use” cases and how to implement them. To provide practical context for readers comprehensive code examples and system architectures are provided.
For high-level strategic folk towards the top of the IT food chain (CIOs, we’re looking at you here), this book may still be useful since it provides an overview of knowledge graphs and how they are delivered.
Sponsored by Neo4j.