In this explanation, we will focus on the “splink” library, which is a probabilistic record linkage library. One of the key features of Splink is the ability to implement various blocking techniques to improve the efficiency of record linkage. In this section, we will explain different blocking techniques and how to implement them effectively using Splink.
Blocking is a technique used to reduce the number of pairwise comparisons between records, which can be computationally expensive. By grouping records into blocks based on certain criteria, we can significantly reduce the number of comparisons required to link records. Splink provides several built-in blocking techniques, as well as the ability to implement custom blocking rules.
Built-in blocking techniques
Splink provides the following built-in blocking techniques:
- Exact matching: This blocking technique matches records based on exact matches of one or more fields. For example, we can block records based on an exact match of the last name and postcode fields.
Example:
blocking_rule_1 = sql.ExactMatch(fields=['last_name', 'postcode'])
- Metaphone blocking: This blocking technique matches records based on the Metaphone phonetic encoding of one or more fields. This can be useful for blocking records with similar but not identical spellings.
Example:
blocking_rule_2 = sql.Metaphone(fields=['first_name'], distance=2)
- Q-gram blocking: This blocking technique matches records based on the Jaro-Winkler similarity of Q-grams of one or more fields. This can be useful for blocking records with spelling errors or typos.
Example:
blocking_rule_3 = sql.QGram(fields=['last_name'], q=2)
