Unveiling the Magic of Probabilistic Record Linkage with Transformer Models in Three Stages

Have you ever wondered how seemingly disparate data can be seamlessly connected to paint a coherent picture? Welcome to the fascinating world of record linkage, where technology meets data to extract powerful insights. In this blog, we delve into a transformative project leveraging probabilistic linkage with transformer models, carried out through a streamlined three-stage process.

Stage 1: Upload the Dataset

Our journey begins with uploading the dataset. To access the application, users need to log in with their registered username and retrieve a verification code from their email to ensure security. Once logged in, the record linkage test data file is ready to be loaded. This public dataset has been used extensively by researchers as it contains various entity information like names, birthdates, row numbers (recID), and entity IDs (entID). It is noteworthy that the entity information consists of simulated data, and any resemblance to an actual person is purely coincidental.

What makes this project unique is the fusion of probabilistic linkage and transformer models, ensuring high accuracy. The frontend is powered by Streamlit, using APIs to manage backend logic and data storage. While the dataset is publicly available, its raw form can be puzzling without linkage, which brings us to the essence of our project: linking records to decode the data.

Fig. 1. App is ready for Recordlinkage test file upload

Fig. 2. Recordlinkage test file upload completed with summary statistics.

Stage 2: Linking the Records

With the dataset uploaded successfully in Stage 1, we proceed to Stage 2 — linking the records. This is where the magic happens. By selecting the necessary columns for linkage, and deploying our advanced models, the records are linked in just a few minutes. However, it’s crucial to remember that the processing time is proportional to the dataset’s size; larger datasets require more time to process.

Fig. 3. Processing data, embedding and linking records

Fig. 4. Recordlinkage completed with runtime.

The outcome of this stage is a treasure trove of connected records, ready to be explored and analyzed. The data linkage process not only streamlines record matching but opens the door to an ocean of insights previously hidden in unlinked information.

Stage 3: Basic and Advanced Search

Venturing into Stage 3, we explore both basic and advanced search functionalities. Starting with a simple task — searching for the name “MONIKA” or “FRITZ”— users can witness the power of our linkage model. Our system generates an ad hoc query based on probabilistic linkage within transformer model thresholds.

Fig. 5. Basic search

Fig. 6. Advance search using Text to SQL.

The search results display the found records along with their matching counterparts, scored for similarity. It also detects duplicate records, and even minor discrepancies, like spelling variations, don’t hinder the linkage process, illustrating the robustness of our method.

Through this streamlined process, we transform complex datasets into invaluable assets, powering innovation and understanding on levels previously unimaginable.

Conclusion

The magic of record linkage is just a few steps away! With each stage of our project, we’re bridging gaps and unlocking potential one dataset at a time.

To try it out, check out the trial version on Docker Hub or watch the demo on YouTube. To customize fill out the questionnaire, and for all other inquiries, contact us. And, stay tuned for more innovations as we continue this exhilarating journey into the world of data technology!