THE BLUEPRINT
Building knowledge graphs :
A practitioner ’ s guide
DOWNLOAD
WHITEPAPER HERE :
Graph databases and graph data science have reached a significant level of adoption . They have been extensively used for a range of discrete use cases like logistics , recommendations and fraud detection . But there is a bigger emerging trend to arrange data in a deliberate manner that enables insight at scale across functional silos . The technology underpinning this trend is know as a knowledge graph .
The forces behind the trend are clear : organisations are no longer suffering from data scarcity . In fact , in an era when Big Data seems to be a solved problem ( at least from a storage point of view ), many organisations are practically drowning in data . Industry anecdotes of many thousands of relational tables per day being ingested into a data lake abound , but with an abundance of data there comes the unexpected challenge of what to with it . This is where knowledge graphs help .
A knowledge graph is a purposeful arrangement of data such that information is put in context and insight is readily available . Individual records are placed in an associative network of relationships that provide rich semantic connectivity and context . That network of relationships – a graph – is an incredibly intuitive way of representing useful knowledge . Data that might have originally existed to serve a frauddetection use case can be repurposed seamlessly within the knowledge graph to provide data for recommending financial products ( or vice versa ). And from there it is straightforward to connect other data to support other vertical use cases or horizontal analysis .
Importantly , while the term knowledge graph has only come to prominence in industry relatively recently , knowledge graph systems have been in existence for some time . This book tries to distil our experience of understanding knowledge graphs deployed in real systems by organisations around the world . It addresses the emerging trend of building systems on knowledge graphs as well as thinking about knowledge graphs as a general-purpose underlay for the enterprise . It also addresses the contemporary intersection of knowledge graphs and Artificial Intelligence ( AI ), where knowledge graphs provide high-quality features for Machine Learning , are themselves enriched by AI , and can even tame the hallucinatory nature of large language models ( LLMs ).
While this is our most in-depth and unapologetically technical book on the topic , this isn ’ t our first time writing about knowledge graphs . In fact , in the book Knowledge Graphs : Data in Context for Responsive Businesses ( O ’ Reilly ), we highlighted the business benefits of knowledge graph adoption aimed at an audience of CIOs and CDOs . But this book goes much deeper technically , and it contains enough implementation detail for a range of tools , patterns and practices so that you can build your own knowledge graphs with confidence . We hope that what you learn here will propel you to your first successful knowledge graph project and beyond ! �
PRESENTED BY
14 www . intelligentbuild . tech