Designing and Building Enterprise Knowledge Graphs

Description

This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice. It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers. Preface Foreword by an Anonymous CDO Foreword by Tom Plasterer Acknowledgments Disclaimer Introduction What is the Problem? Spreadsheet Approach Query Approach Data Warehouse Approach Data Lake Approach Data Wrangling Approach So What? Knowledge Graphs What is a Knowledge Graph? Why Knowledge Graphs? Why Now? Background History of Knowledge Graphs Semantics Semantic Web Models, Ontologies, and Schemata Why This Book? Designing Enterprise Knowledge Graphs Source: Relational Databases Target: Knowledge Graph RDF Graph Property Graph Knowledge Graph Schema An Abstract Graph Notation Used in This Book Graph Query Languages Identifiers Modeling Mappings: Relational Database to Knowledge Graph Direct Mapping Custom Mapping Mapping Languages Mapping Design Patterns Direct Custom Mapping Patterns Direct Concept Direct Concept Attribute Direct Relationship Direct Relationship Attribute Complex Custom Concept Mapping Patterns Complex Concept: Conditions Complex Concept: Data as a Concept Complex Concept: Join Complex Concept: Distinct Complex Custom Attribute Mapping Patterns Complex Concept Attribute: CONCAT Complex Concept Attribute: Math Complex Concept Attribute: CASE Complex Concept Attribute: NULL Complex Concept Attribute: JOIN Complex Concept Attribute: LEFT JOIN Complex Concept Attribute: Duplicate Complex Concept Attribute: Constant Table Complex Concept Attribute: Constant Attribute Complex Concept Attribute: Constant Value Complex Concept Attribute: EAV Complex Custom Relationship Mapping Patterns Relationship: Many to Many Relationship: One to Many without Duplicates Relationship: One to Many with Duplicates Relationship: One to One with Duplicates Relationship: Constant Table Relationship: Constant Attribute Relationship: Constant Value Relationship: Bidrectional Building Enterprise Knowledge Graphs People Data Producers and Consumers Data Product Manager Process Phase 1: Knowledge Capture Phase 2: Knowledge Implementation Phase 3: Knowledge Access An E-Commerce Use Case Tools Metadata Management Knowledge Management Data Management Additional Tools What's Next? Couldn't I Have Done This with a Relational Database? Isn't this Just Master Data Management? Knowledge Graphs and AI Symbolic Reasoning Non-Symbolic Reasoning Conclusions It's All a Graph! Mapping Patterns You Need a Data Team Be Agile, Start Small, Don't Boil the Ocean Bibliography Authors' Biographies Blank Page Blank Page