Building Knowledge Graphs 6th Early Release 1st Edition by Jesus Barrasa, Jim Webber ISBN 9781098127091 1098127099

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Product details:
ISBN 10: 1098127099
ISBN 13: 9781098127091
Author: Jesus Barrasa, Jim Webber

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 saving interlinked descriptions of entities (objects, events, situations, or abstract concepts) while encoding the semantics underlying the terminology. How do you create a knowledge graph? And how do you move it from theory into practice?
Graph data has become ubiquitous in the last decade. Graphs underpin everything from consumer-facing systems like navigation and social networks to critical infrastructure like supply chains and policing. A consistent theme has emerged: applying knowledge in context is the single most powerful tool that most businesses have. Through research and experience, a set of patterns and practices called knowledge graphs has been developed to support extracting knowledge from data of all types and in all sources, from systems of record to frozen data lakes to application logs. This book is for information technology professionals who are interested in building and operating knowledge graphs within their businesses.
Using hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa, Maya Natarajan, and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs that solve many of today’s pressing problems. You’ll quickly discover how these graphs become exponentially more useful as you add more data.
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
Understand what pattern detection knowledge graphs help you accomplish
Explore dependency knowledge graphs through examples
Use examples of natural language knowledge graphs and chatbots

Building Knowledge Graphs 6th Early Release 1st Edition Table of contents:

1. Introducing Knowledge Graphs
What Are Graphs?
The Motivation for Knowledge Graphs
Knowledge Graphs: A Definition
Summary
2. Organizing Principles for Building Knowledge Graphs
Organizing Principles of a Knowledge Graph
Plain Old Graphs
Richer graph models
Knowledge Graphs using Taxonomies for Hierarchy
Knowledge Graphs Using Ontologies for Multilevel Relationships
Which Is the Best Organizing Principle for Your Knowledge Graph?
Organizing Principles: Standards Versus Create Your Own
Creating Your Own Organizing Principle
Essential Characteristics of a Knowledge Graph
Summary
3. Graph Databases
The Cypher Query language
Creating Data in a Knowledge Graph
Avoiding duplicates when enriching a Knowledge Graph
Graph Local Queries
Graph Global Queries
Calling Functions and Procedures
Supporting Tools for Writing Knowledge Graph Queries
Neo4j Internals
Query Processing
ACID Transactions
Summary
4. Loading Knowledge Graph Data
Loading Data with the Neo4j Data Importer
Online Bulk Data Loading with LOAD CSV
Initial Bulk Load
Summary
5. Integrating Knowledge Graphs with Information Systems
Towards a data fabric
The Database Driver
Graph Federation with Composite Databases
Server-side Procedures
Data Virtualization with Neo4j APOC
Custom Functions and Procedures
Complementary Tools and Techniques
GraphQL
Kafka Connect Plugin
Neo4j Spark Connector
Apache Hop for ETL
Summary
6. Enriching Knowledge Graphs with Data Science
Why Graph Algorithms?
Different Classes of Graph Algorithms
Graph Data Science operations
Experimenting with Graph Data Science
Production Considerations
Enriching the Knowledge Graph
Summary
7. Graph-Native Machine Learning
Machine Learning in a Nutshell
Topological Machine Learning
Graph-Native Machine Learning Pipelines
Recommending Complementary Actors
Summary
8. Mapping data with Metadata Knowledge Graphs
The challenge of distributed data stewardship
Datasets connected to data platforms
Tasks and data pipelines
Data sinks
Metadata graph example
Querying the Metadata Graph Model
Using Relationships to Connect Data and Metadata
Summary
9. Identity Knowledge Graphs
Knowing Your Customer
When does the problem appear?
Graph-based Entity Resolution step by step
Data preparation:
Entity matching
Build/update a persisted record of master entities
Working with unstructured data
Summary
10. Pattern Detection Knowledge Graphs
Fraud Detection
First-Party Fraud
Uncovering Fraud from Data
Fraud Rings
Innocent Bystanders
Operationalizing the Fraud Detection Knowledge Graph
Skills Matching
Organizational Knowledge Graph
Skills Knowledge Graph
Expertise Knowledge Graph
Individual Career Growth
Organizational Planning
Predicting Organizational Performance
Summary
11. Dependency Knowledge Graphs
Dependencies as a Graph
Advanced Graph Dependency Modeling
Qualified Dependencies
Semantics of Multi-Dependency
Impact Propagation with Cypher
Validating a Dependency Knowledge Graph
Complex Dependency Processing
Single Point of Failure Analysis
Root Cause Analysis
Summary
12. Semantic Search and Similarity
Search over unstructured data
From strings to things: Annotating documents with entities
Navigating the connections: document similarity for recommendations
The cold start problem
Making the Annotation Semantic with an Organizing Principle
Summary
13. Talking to Your Knowledge Graph
Question Answering: Natural Language as a Source of Facts for a Knowledge Graph
Using Natural Language Query with a Knowledge Graph
Natural Language Generation from Knowledge Graphs
Annotating the Knowledge Graph’s Organizing Principle to Drive Natural Language Generation
Working with Lexical Databases
Graph-Based Semantic Similarity
Path Similarity
Leacock-Chodorow Similarity
Wu & Palmer Similarity
Summary
14. From Knowledge Graphs to Knowledge Lakes
Conventional Knowledge Graph Deployments
From knowledge graphs to knowledge lakes
Looking to the future
About the Authors

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Tags: Jesus Barrasa, Jim Webber, Knowledge Graphs