Entity resolution in databases can be challenging due to duplicate records with different representations and errors. Whether it is a Bank trying to identify risky new customers by looking them up against published Suspicious Matter Reports (SMRs) or an Enterprise trying to build a Customer Intelligence system to improve sales and marketing, Entity Resolution is a vital part of any organization’s data enrichment initiatives. In today’s data-driven world, the ability to accurately match and link different data sets is crucial for effective decision-making. It can help organizations identify fraud, improve customer experience, and enhance their overall business operations.
In this blog, we are going to discuss a novel and improved approach to implementing Entity Resolution frameworks using LLMs and Vector Databases. By leveraging these advanced technologies, organizations can achieve even greater accuracy and efficiency in their Entity Resolution processes. Ultimately, leading to better business decisions.
What is Entity Resolution?
By definition, “Entity Resolution is the task of finding records in a data set that refer to the same entity across different data sources (e.g., data files, books, websites, and databases).” It is also referred to as De-duplication and encompasses calculating the similarity between two entities from different systems.
In other words, Entity resolution involves comparing various attributes or features of the records, such as names, addresses, phone numbers, or other identifying information, to determine the likelihood of a match. As most of that data is stored as text, typographical differences, are a frequent cause of mismatches in the database. There are multiple similarity or distance evaluation metrics available such as character-based similarity, token-based similarity, and phonetic similarity or you can also recognise duplicate detection as a Bayesian inference problem. The choice of these metrics depends on the specific domain, use case and characteristics of the representation.
What are the current limitations?
Current entity resolution (ER) frameworks are limited for the following reasons:
- Data Source Constraints: Most enterprise data sources, such as relational database management systems (RDBMS) and file storage systems, do not natively support text processing. Most, if not all, use cases for ER are heavily dependent on text processing, but their data sources are not conducive to the activities involved in entity resolution. To overcome this limitation, new data sources designed to support text processing may need to be explored.
- System Constraints: Text processing transactions are hardware intensive, and ER datasets are generally large. This causes a hardware crunch on both fronts – when loading the datasets into application memory and during algorithm runtime memory. One way to address this limitation is to optimize the algorithms and hardware to handle large datasets more efficiently.
Due to the above limitations, ER frameworks may experience slow processing times, be bulky, prone to failure, and take longer to realize value. However, with advances in technology and innovative solutions, these limitations can be overcome and ER frameworks can become more efficient and effective.
Improving similarity calculations with Large Language Models (LLMs)
The fundamental building blocks of Entity Resolution deal with calculating the similarity between two textual entities. Pre-trained LLMs provide the advantage of bringing in the intelligence around semantic relationships between words and also the ability to work with unprocessed text data with spelling mistakes and grammatical errors. This significantly enhances fuzzy matching algorithms and rule-based systems used in current ER systems, which require continuous maintenance to avoid becoming outdated rapidly. The other benefits of LLMs are textual understanding, contextual similarity, flexible representation learning, transfer learning and scalability.
Enhanced scalability with Vector Databases
Vector databases store data as vectors, which encode the essential characteristics and features of entities. By representing entities as vectors, complex attributes and relationships can be captured in a compact and numerical form, facilitating efficient matching and similarity computations. Vector Databases provide native support for the similarity algorithms between vector-based data types such as k-nearest neighbours (k-NN) and approximate nearest neighbours (ANN). Entity resolution often involves dealing with high-dimensional data. Vector databases are designed to handle such high-dimensional data efficiently, allowing for fast indexing, storage, and retrieval of vectors representing entities. Vector DBs also allow us to store the embeddings from LLMs and process the embeddings for similarity without the need of loading the data into application memory, improving the latency.
Conclusion
Using bleeding-edge innovations like LLMs and Vector Databases, this novel approach for Entity Resolution will overcome the problems plaguing legacy implementations and provide fast and scalable solutions for Enterprises. As entity resolution continues to play a vital role in data management, the integration of vector databases and LLMs holds immense potential for advancing the field.
If you are an enterprise looking to implement LLM and Vector Database solutions, feel free to book a consultation using the link.
For code example, please visit – github
