Hands-On Entity Resolution: A Practical Guide to Data Matching With Python

Copyright
Table of Contents
Preface
	Who Should Read This Book
	Why I Wrote This Book
	Navigating This Book
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
Chapter 1. Introduction to Entity Resolution
	What Is Entity Resolution?
	Why Is Entity Resolution Needed?
	Main Challenges of Entity Resolution
		Lack of Unique Names
		Inconsistent Naming Conventions
		Data Capture Inconsistencies
		Worked Example
		Deliberate Obfuscation
		Match Permutations
		Blind Matching?
	The Entity Resolution Process
		Data Standardization
		Record Blocking
		Attribute Comparison
		Match Classification
		Clustering
		Canonicalization
		Worked Example
	Measuring Performance
	Getting Started
Chapter 2. Data Standardization
	Sample Problem
	Environment Setup
	Acquiring Data
		Wikipedia Data
		TheyWorkForYou Data
	Cleansing Data
		Wikipedia
		TheyWorkForYou
	Attribute Comparison
	Constituency
	Measuring Performance
	Sample Calculation
	Summary
Chapter 3. Text Matching
	Edit Distance Matching
		Levenshtein Distance
		Jaro Similarity
		Jaro-Winkler Similarity
	Phonetic Matching
		Metaphone
		Match Rating Approach
	Comparing the Techniques
	Sample Problem
	Full Similarity Comparison
	Measuring Performance
	Summary
Chapter 4. Probabilistic Matching
	Sample Problem
	Single Attribute Match Probability
		First Name Match Probability
		Last Name Match Probability
	Multiple Attribute Match Probability
	Probabilistic Models
		Bayes’ Theorem
		m Value
		u Value
		Lambda (  λ) Value
		Bayes Factor
		Fellegi-Sunter Model
		Match Weight
	Expectation-Maximization Algorithm
		Iteration 1
		Iteration 2
		Iteration 3
	Introducing Splink
		Configuring Splink
		Splink Performance
	Summary
Chapter 5. Record Blocking
	Sample Problem
	Data Acquisition
		Wikipedia Data
		UK Companies House Data
	Data Standardization
		Wikipedia Data
		UK Companies House Data
	Record Blocking and Attribute Comparison
		Record Blocking with Splink
		Attribute Comparison
	Match Classification
	Measuring Performance
	Summary
Chapter 6. Company Matching
	Sample Problem
	Data Acquisition
	Data Standardization
		Companies House Data
		Maritime and Coastguard Agency Data
	Record Blocking and Attribute Comparison
	Match Classification
	Measuring Performance
	Matching New Entities
	Summary
Chapter 7. Clustering
	Simple Exact Match Clustering
	Approximate Match Clustering
	Sample Problem
		Data Acquisition
		Data Standardization
	Record Blocking and Attribute Comparison
		Data Analysis
		Expectation-Maximization Blocking Rules
	Match Classification and Clustering
	Cluster Visualization
	Cluster Analysis
	Summary
Chapter 8. Scaling Up on Google Cloud
	Google Cloud Setup
		Setting Up Project Storage
	Creating a Dataproc Cluster
	Configuring a Dataproc Cluster
	Entity Resolution on Spark
	Measuring Performance
	Tidy Up!
	Summary
Chapter 9. Cloud Entity Resolution Services
	Introduction to BigQuery
	Enterprise Knowledge Graph API
		Schema Mapping
		Reconciliation Job
		Result Processing
		Entity Reconciliation Python Client
	Measuring Performance
	Summary
Chapter 10. Privacy-Preserving Record Linkage
	An Introduction to Private Set Intersection
	How PSI Works
	PSI Protocol Based on ECDH
		Bloom Filters
		Golomb-Coded Sets
	Example: Using the PSI Process
		Environment Setup
		Server Code
		Client Code
		Full MCA and Companies House Sample Example
	Summary
Chapter 11. Further Considerations
	Data Considerations
		Unstructured Data
		Data Quality
		Temporal Equivalence
	Attribute Comparison
		Set Matching
		Geocoding Location Matching
		Aggregating Comparisons
	Post Processing
	Graphical Representation
	Real-Time Considerations
	Performance Evaluation
		Pairwise Approach
		Cluster-Based Approach
	Future of Entity Resolution
Index
About the Author
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