Record linkage, also known as data matching or data fusion, is the task of identifying and linking records that refer to the same entities across different data sources. The splink_demos project provides tools and examples for record linkage tasks, using the Splink library. In this explanation, we will discuss the possible options and provide examples for each option, using the provided documentation and code snippets.
Possible options for record linkage tasks
- Linking records based on exact matching: This is the simplest form of record linkage, where records are linked if all the matching fields have identical values in both records. For example, linking customer records based on exact matches of first name, last name, and date of birth.
Example: In the tutorials/00_Tutorial_Introduction.ipynb notebook, the create_training_data() function demonstrates how to create training data for a linkage model based on exact matching of first name, last name, and date of birth.
- Linking records based on probabilistic matching: This method is used when exact matching is not possible or reliable, due to errors, inconsistencies, or missing values in the data. Probabilistic matching uses statistical methods to estimate the likelihood of two records referring to the same entity, based on the similarity of their matching fields.
Example: In the tutorials/07_Quality_assurance.ipynb notebook, the run_quality_assurance() function demonstrates how to evaluate the quality of a linkage model based on probabilistic matching, using metrics such as precision, recall, and F1 score.
- Linking records based on machine learning models: This method uses machine learning algorithms to learn the patterns and features that distinguish between true matches and false matches, based on the similarity of their matching fields. Machine learning models can handle more complex and nuanced matching scenarios, such as handling missing or partial data, or dealing with different data types and formats.
Example: In the tutorials/07_Quality_assurance.ipynb notebook, the train_logistic_regression_model() function demonstrates how to train a logistic regression model for linkage, using the sklearn library.
- Linking records based on fuzzy matching: This method uses string matching algorithms that can handle variations and errors in the spelling, formatting, or encoding of the matching fields. Fuzzy matching can be useful when dealing with noisy or unstructured data, such as text, names, or addresses.
Example: In the tutorials/scv.html notebook, the fuzzy_match() function demonstrates how to perform fuzzy matching of strings using the fuzzywuzzy library.
- Linking records based on spatial or temporal proximity: This method uses the location or time information associated with the records, to estimate their spatial or temporal proximity. Spatial or temporal proximity can be a strong indicator of a true match, especially when dealing with geographical or temporal data.
Example: In the tutorials/cluster_studio.html notebook, the create_spatial_linkage_model() function demonstrates how to create a linkage model based on spatial proximity, using the geopy library.
In summary, the splink_demos project provides various options and examples for record linkage tasks, using the Splink library. Depending on the specific requirements and characteristics of the data, users can choose the most appropriate method or combination of methods for their linkage task.
