Steps of Supported EM Workflows

• Reading the CSV Files from Disk
• Down Sampling
• Profiling Data
  • Example Usage
• Data Exploration
  • OpenRefine
    • Using OpenRefine
  • Pandastable
    • Using pandastable
• Specifying Blockers and Performing Blocking
  • Types of Blockers and Blocker Hierarchy
  • Built-In Blockers
  • Blackbox Blockers
  • Rule-Based Blockers
  • Combining Multiple Blockers
• Creating Features for Blocking
  • Available Tokenizers and Similarity Functions
  • Obtaining Tokenizers and Similarity Functions
  • Obtaining Attribute Types and Correspondences
  • Getting a Set of Features
  • Adding/Removing Features
  • Summary of the Manual Feature Generation Process
  • Ways to Edit the Manual Feature Generation Process
  • Generating Features Automatically
• Debugging Blocking
• Sampling
• Labeling
• Splitting Labeled Data into Training and Testing Sets
• Creating Features for Matching
  • Creating the Features Manually
  • Creating the Features Automatically
• Extracting Feature Vectors
• Imputing Missing Values
• Specifying Matchers and Performing Matching
  • ML-Matchers
    • Creating Learning-Based Matchers
    • Training Learning-Based Matchers
    • Applying Learning-Based Matchers
  • Rule-Based Matchers
    • Adding and Deleting Rules
    • Applying Rule-Based Matcher
• Selecting a ML-Matcher
• Debugging ML-Matchers
  • Debugging Using the GUI
  • Debugging Using the Command Line
  • Impact of Imputing Missing Values
• Combining Predictions from Multiple Matchers
• Using Triggers to Update Matching Results
  • Match Triggers
    • Creating the Trigger
    • Adding and Deleting Rules
    • Executing the Triggers
• Evaluating the Matching Output