Knowledge Graphs: Data in Context for Responsive BusinessesКНИГИ » ОС И БД
Название: Knowledge Graphs: Data in Context for Responsive Businesses Автор: Jesus Barrasa, Amy E. Hodler, Jim Webber Издательство: O’Reilly Media, Inc. Год: 2021-07-26 Язык: английский Формат: epub Размер: 10.1 MB
The winners in the data industry know where the puck is going: making data smarter. This can be accomplished by integrating data with knowledge at scale, and this is where knowledge graphs come in. This book is a practical guide to understand what knowledge graphs are and why you should care. Importantly, it strikes the right balance between technical aspects and corresponding business value for an organization. If you need to make the business case for knowledge graphs, this is the book for you!
Let’s first talk about the elephant in the room: RDF versus property graphs. Over the years, I’ve enjoyed my conversations with Jesús Barrasa on this topic. We have always been strong believers that these technologies will converge because at the end of the day, it’s all just a graph! This book is evidence of this convergence: enriching the property graph model with taxonomies, ontologies, and semantics in order to create knowledge graphs. And don’t forget that the conversation should focus on the business value and not just the technology.
Applying knowledge in the right context is the most powerful lever businesses can use to become agile, creative, and resilient. Knowledge graphs add context, meaning, and utility to business data. They drive intelligence into data for unparalleled automation and visibility into processes, products, and customers. Businesses use knowledge graphs to anticipate downstream effects, make decisions based on all relevant information, and quickly respond to dynamic markets.
Speaking of AI, knowledge graphs are changing AI by providing context. This leads to explainability, diversification, and improved processing. If AI is changing the future and knowledge graphs are changing AI, then by transitivity, knowledge graphs are also changing the future.
This report is for information technology professionals who are interested in managing and exploiting data for value. For the CIO or CDO, the report is brief yet thorough enough to provide an overview of the techniques and how they are delivered. For the data professional, data scientist, or software professional, this report provides an easy on-ramp to the world of knowledge graphs, and a language for discussing their implementation with peers and management.
Foreword 1. Introduction What Are Graphs? The Motivation for Knowledge Graphs Knowledge Graphs: A Definition 2. Building Knowledge Graphs 3. Data Management for Actionable Knowledge Relationships and Metadata Make Knowledge Actionable The Actioning Knowledge Graph The Data Fabric Architecture Metadata Management Popular Use Cases for Actioning Knowledge Graphs Increased Trust and Radical Visibility 4. Data Processing for Driving Decisions Data Discovery and Exploration The Predictive Power of Relationships The Decisioning Knowledge Graph Graph Queries Graph Algorithms Graph Embeddings ML Workflows for Graphs Graph Visualization Decisioning Knowledge Graph Use Cases Boston Scientific’s Decisioning Graph Better Predictions and More Breakthroughs 5. Contextual AI Why AI Needs Context Data Provenance and Tracking for AI Systems Diversifying ML Data Better ML Processes Improving AI Reasoning The Big Picture 6. Business Digital Twin 7. The Way Forward
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