Building Knowledge Graphs: A Practitioner’s Guide
by Jesus Barrasa(Author), Jim Webber(Author)
Publisher finelybook 出版社: O’Reilly Media; (August 1, 2023)
Language 语言: English
Print Length 页数: 288 pages
ISBN-10: 1098127102
ISBN-13: 9781098127107
Book Description
By finelybook
Incredibly useful, knowledge graphs help organizations keep track of medical research, cybersecurity threat intelligence, GDPR compliance, web user engagement, and much more. They do so by storing interlinked descriptions of entities―objects, events, situations, or abstract concepts―and encoding the underlying information. How do you create a knowledge graph? And how do you move it from theory into production?
Using hands-on examples, this practical book shows data scientists and data engineers how to build their own knowledge graphs. Authors Jesús Barrasa and Jim Webber from Neo4j illustrate common patterns for building knowledge graphs that solve many of today’s pressing knowledge management problems. You’ll quickly discover how these graphs become increasingly useful as you add data and augment them with algorithms and machine learning.
Learn the organizing principles necessary to build a knowledge graph
Explore how graph databases serve as a foundation for knowledge graphs
Understand how to import structured and unstructured data into your graph
Follow examples to build integration-and-search knowledge graphs
Learn what pattern detection knowledge graphs help you accomplish
Explore dependency knowledge graphs through examples
Use examples of natural language knowledge graphs and chatbots
Use graph algorithms and ML to gain insight into connected data
Who This Book Is For
This is a technical book, aimed at computing professionals—typically software engineers, system architects, and techincal managers—who want to understand both the potential of knowledge graphs and how to go about implementing them. While no prior experience with knowledge graphs (or graphs in the general sense) is required, readers will get the most of the book if they are modestly comfortable with database concepts like queries and have some programming experience.
From the Preface
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: organizations 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 organizations 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 fraud-detection 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 distill our experience of understanding knowledge graphs deployed in real systems by organizations 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).
About the Author
Dr. Jesus Barrasa, an expert in semantic technologies and graph databases, is head of the solutions architecture team in EMEA at Neo4j and leads the development of neosemantics (a Neo4j plugin for RDF). He cowrote Knowledge Graphs: Data in Context for Responsive Businesses (O’Reilly) and is cohost of the Going Meta live webcast.
Dr. Jim Webber is chief scientist at Neo4j, where he works on fault-tolerant graph databases. He coauthored Graph Databases for Dummies (Wiley) and Graph Databases and Knowledge Graphs: Data in Context for Responsive Businesses, both for O’Reilly. He’s also a visiting professor at Newcastle University, UK.
