I am trying to find record matches between two sets of data using supervised machine learning for the classification of potential matches.
I have two sets of data each with their own categories which are non mutually exclusive and not equivalent. e.g
Set A
| Name | Species | Cuteness |
|---|---|---|
| Alice | Cat | Very |
| Bob | Snake | No |
| Sara | Dog | Yes |
Set B
| Name | Breed |
|---|---|
| A. | Shorthair |
| Robert | GrassSnake |
| Sara | Labrador |
For this example, in the training data of true matches I imagine there will be a pattern of certain 'Breeds' being more likely to be cute or not, even though it is subjective.
I have been using record linkage in Python so far, for comparison of strings/numerics and supervised classification.
I have created each category/answer as a binary table as below. Although I'm not sure how this type of comparison/classification can be done.
Set A
| Name | Cat| Dog | Snake | etc
| -----| ---|-----|-------|--
| Alice| 1 | 0 | 0 |
| Bob | 0 | 1 | 0 |
| Sara | 0 | 0 | 1 |
Record linkage is the process of identifying and linking records that refer to the same entity across different data sources. One of the challenges of record linkage is to deal with non-equivalent categories, which are categories that have different names or labels but represent the same concept. For example, the category "USA" in one dataset might be equivalent to the category "United States of America" in another dataset.
There are different methods to match non-equivalent categories in two datasets for record linkage using Python. Here are three possible methods:
One simple method is to create a dictionary that maps the non-equivalent categories from one dataset to the other. For example, if we have two datasets with country names, we can create a dictionary like this:
country_map = {
"USA": "United States of America",
"UK": "United Kingdom",
"UAE": "United Arab Emirates",
# and so on
}
Then, we can use this dictionary to replace the non-equivalent categories in one of the datasets with the corresponding ones in the other dataset. For example, if we want to match the country names in dataset1 with the ones in dataset2, we can do something like this:
# assume dataset1 and dataset2 are pandas dataframes with a column named "country"
dataset1["country"] = dataset1["country"].apply(lambda x: country_map.get(x, x))
# this will replace the non-equivalent categories in dataset1 with the ones in dataset2
# if the category is not in the dictionary, it will keep the original value
This method is easy to implement and fast to execute, but it requires manual creation and maintenance of the dictionary, which can be tedious and error-prone. It also does not handle spelling errors, typos, or variations in the category names.
Another method is to use fuzzy matching, which is a technique that measures the similarity between two strings based on some criteria, such as edit distance, soundex, or n-grams. Fuzzy matching can help to find categories that are similar in spelling or pronunciation, but not exactly the same. For example, the category "USA" might be similar to the category "U.S.A." or "US".
There are different libraries in Python that can perform fuzzy matching, such as fuzzywuzzy, difflib, or jellyfish. For example, using fuzzywuzzy, we can do something like this:
from fuzzywuzzy import process
# assume dataset1 and dataset2 are pandas dataframes with a column named "country"
# get the unique categories in dataset2
categories = dataset2["country"].unique()
# for each category in dataset1, find the most similar category in dataset2
dataset1["country"] = dataset1["country"].apply(lambda x: process.extractOne(x, categories)[0])
# this will replace the categories in dataset1 with the most similar ones in dataset2
# based on the Levenshtein distance
This method is more flexible and robust than the dictionary method, as it can handle spelling errors, typos, or variations in the category names. However, it is also more computationally expensive and slower, and it might not always find the correct match, especially if the categories are very different in meaning or structure.
A third method is to use semantic matching, which is a technique that measures the similarity between two strings based on their meaning or context, rather than their form or appearance. Semantic matching can help to find categories that are equivalent in concept, but not in expression. For example, the category "USA" might be equivalent to the category "The country with 50 states and a president".
One way to perform semantic matching in Python is to use the sentence_transformers library, which is a framework that allows us to use pre-trained models to generate sentence embeddings, which are numerical representations of the meaning of sentences. We can then compare the embeddings of the categories using some metric, such as cosine similarity, to find the most similar ones.
For example, using sentence_transformers, we can do something like this:
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
# assume dataset1 and dataset2 are pandas dataframes with a column named "country"
# load a pre-trained model
model = SentenceTransformer("paraphrase-MiniLM-L6-v2")
# get the embeddings of the categories in dataset2
categories = dataset2["country"].unique()
embeddings = model.encode(categories)
# for each category in dataset1, find the most similar category in dataset2
dataset1["country"] = dataset1["country"].apply(lambda x: categories[cosine_similarity(model.encode([x]), embeddings).argmax()])
# this will replace the categories in dataset1 with the most similar ones in dataset2
# based on the cosine similarity of their embeddings
This method is more advanced and powerful than the previous methods, as it can capture the semantic equivalence of the categories, even if they are expressed in different ways. However, it is also more complex and resource-intensive, and it requires a suitable pre-trained model that can handle the domain and language of the categories. It might also not always find the correct match, especially if the categories are ambiguous or have multiple meanings.