Building Knowledge Graphs

Book description

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 Jesus 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

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Table of contents

  1. Preface
    1. Who This Book Is For
    2. Conventions Used in This Book
    3. O’Reilly Online Learning
    4. How to Contact Us
    5. Acknowledgments
  2. 1. Introducing Knowledge Graphs
    1. What Are Graphs?
    2. The Motivation for Knowledge Graphs
    3. Knowledge Graphs: A Definition
    4. Summary
  3. 2. Organizing Principles for Building Knowledge Graphs
    1. Organizing Principles of a Knowledge Graph
      1. Plain Old Graphs
      2. Richer Graph Models
      3. Knowledge Graphs Using Taxonomies for Hierarchy
      4. Knowledge Graphs Using Ontologies for Multilevel Relationships
    2. Which Is the Best Organizing Principle for Your Knowledge Graph?
    3. Organizing Principles: Standards Versus Create Your Own
      1. Creating Your Own Organizing Principle
    4. Essential Characteristics of a Knowledge Graph
    5. Summary
  4. 3. Graph Databases
    1. The Cypher Query Language
      1. Creating Data in a Knowledge Graph
      2. Avoiding Duplicates When Enriching a Knowledge Graph
      3. Graph Local Queries
      4. Graph Global Queries
      5. Calling Functions and Procedures
      6. Supporting Tools for Writing Knowledge Graph Queries
    2. Neo4j Internals
      1. Query Processing
      2. ACID Transactions
    3. Summary
  5. 4. Loading Knowledge Graph Data
    1. Loading Data with the Neo4j Data Importer
    2. Online Bulk Data Loading with LOAD CSV
    3. Initial Bulk Load
    4. Summary
  6. 5. Integrating Knowledge Graphs with Information Systems
    1. Towards a Data Fabric
    2. The Database Driver
    3. Graph Federation with Composite Databases
    4. Server-Side Procedures
    5. Data Virtualization with Neo4j APOC
    6. Custom Functions and Procedures
    7. Complementary Tools and Techniques
      1. GraphQL
      2. Kafka Connect Plug-In
      3. Neo4j Spark Connector
      4. Apache Hop for ETL
    8. Summary
  7. 6. Enriching Knowledge Graphs with Data Science
    1. Why Graph Algorithms?
    2. Different Classes of Graph Algorithms
    3. Graph Data Science Operations
    4. Experimenting with Graph Data Science
    5. Production Considerations
    6. Enriching the Knowledge Graph
    7. Summary
  8. 7. Graph-Native Machine Learning
    1. Machine Learning in a Nutshell
    2. Topological Machine Learning
    3. Graph-Native Machine Learning Pipelines
    4. Recommending Complementary Actors
    5. Summary
  9. 8. Mapping Data with Metadata Knowledge Graphs
    1. The Challenge of Distributed Data Stewardship
    2. Datasets Connected to Data Platforms
    3. Tasks and Data Pipelines
    4. Data Sinks
    5. Metadata Graph Example
    6. Querying the Metadata Graph Model
    7. Using Relationships to Connect Data and Metadata
    8. Summary
  10. 9. Identity Knowledge Graphs
    1. Knowing Your Customer
    2. When Does the Problem Appear?
    3. Graph-Based Entity Resolution Step by Step
      1. Data Preparation
      2. Entity Matching
      3. Build/Update a Persisted Record of Master Entities
    4. Working with Unstructured Data
    5. Summary
  11. 10. Pattern Detection Knowledge Graphs
    1. Fraud Detection
      1. First-Party Fraud
      2. Uncovering Fraud from Data
      3. Fraud Rings
      4. Innocent Bystanders
      5. Operationalizing the Fraud Detection Knowledge Graph
    2. Skills Matching
      1. Organizational Knowledge Graph
      2. Skills Knowledge Graph
      3. Expertise Knowledge Graph
      4. Individual Career Growth
      5. Organizational Planning
      6. Predicting Organizational Performance
    3. Summary
  12. 11. Dependency Knowledge Graphs
    1. Dependencies as a Graph
    2. Advanced Graph Dependency Modeling
      1. Qualified Dependencies
      2. Semantics of Multidependency
    3. Impact Propagation with Cypher
    4. Validating a Dependency Knowledge Graph
      1. Validation 1: No Cycles
      2. Validation 2: Aggregate Multidependencies Add Up to the Expected Total
    5. Complex Dependency Processing
      1. Single-Point-of-Failure Analysis
      2. Root Cause Analysis
    6. Summary
  13. 12. Semantic Search and Similarity
    1. Search over Unstructured Data
    2. From Strings to Things: Annotating Documents with Entities
    3. Navigating the Connections: Document Similarity for Recommendations
      1. The Cold Start Problem
    4. Making the Annotation Semantic with an Organizing Principle
    5. Summary
  14. 13. Talking to Your Knowledge Graph
    1. Question Answering: Natural Language as a Source of Facts for a Knowledge Graph
    2. Using Natural Language Query with a Knowledge Graph
    3. Natural Language Generation from Knowledge Graphs
      1. Annotating the Knowledge Graph’s Organizing Principle to Drive Natural Language Generation
    4. Working with Lexical Databases
    5. Graph-Based Semantic Similarity
      1. Path Similarity
      2. Leacock-Chodorow Similarity
      3. Wu and Palmer Similarity
    6. Summary
  15. 14. From Knowledge Graphs to Knowledge Lakes
    1. Conventional Knowledge Graph Deployments
    2. From Knowledge Graphs to Knowledge Lakes
    3. Looking to the Future
  16. Index
  17. About the Authors

Product information

  • Title: Building Knowledge Graphs
  • Author(s): Jesus Barrasa, Jim Webber
  • Release date: June 2023
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098127107