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

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
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
2. Data Standardization
   Sample Problem
   Environment Setup
   Acquiring Data
      Wikipedia Data
      TheyWorkForYou Data
         Adding Facebook links
   Cleansing Data
      Wikipedia
      TheyWorkForYou
   Attribute Comparison
   Constituency
   Measuring Performance
   Sample Calculation
   Summary
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
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
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
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
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
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
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
10. Privacy-Preserving Record Linkage
   An Introduction to Private Set Intersection
   How PSI Works
   PSI Protocol Based on ECDH
      Bloom Filters
         Bloom filter example
      Golomb-Coded Sets
         GCS example
   Example: Using the PSI Process
      Environment Setup
         Google Cloud setup
         Option 1: Prebuilt PSI package
         Option 2: Build PSI package
         Server install
      Server Code
      Client Code
         Using raw encrypted server values
         Using Bloom filter–encoded encrypted server values
         Using GCS-encoded encrypted server values
      Full MCA and Companies House Sample Example
   Summary
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