String Comparison Vectors for Large-Scale Record Linkage
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Solution Overview
Problem
Existing record linkage techniques face challenges in efficiently matching large datasets due to computational inefficiencies in string comparisons, particularly in databases containing millions of records, where current methods fail to utilize vectors that inherently include spelling similarity in word embeddings, and are unable to visualize and analyze with vector-based techniques for identifying patterns and other characteristics.
Innovation Solution
Implementing a system that uses modified Levenshtein distance to generate vectors for strings, prioritizing spelling similarity over semantic relationships, and applying weights to insertions, deletions, and replacements, and using vector databases for efficient record linkage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive string comparison is performed for every record pair to achieve highest accuracy, then matching accuracy is improved, but computational time increases exponentially
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing string vectors in a vector database before the actual matching process. This allows the system to quickly retrieve and compare pre-processed representations during record linkage, avoiding the need to perform expensive string comparisons on demand while maintaining high matching accuracy.
Solution Approach 2:
The patent replaces the mechanical string comparison system with a vector-based computational system. Instead of directly comparing strings using traditional algorithms, the system converts strings to vectors and uses vector operations for comparison, significantly reducing computational complexity and time while preserving matching accuracy.
2Productivity
If blocking or clustering techniques are used to reduce computational load, then processing speed is improved, but matching accuracy deteriorates due to missed matches
Solution Approach 1:
The patent introduces vectors as an intermediary representation between the original strings and the comparison process. This intermediary allows for efficient computation without the need for blocking or clustering, as the vector representations capture the essential characteristics of strings for matching purposes while enabling faster operations.
3Adaptability or versatility
If traditional word embedding is used for record linkage, then semantic similarity is captured, but spelling similarity required for record linkage is lost
Solution Approach 1:
The patent applies local quality by customizing the vector embedding process to prioritize spelling similarity over semantic meaning. The system uses a custom loss function and training approach that focuses on capturing character-level similarities and spelling variations, which are locally important for record linkage tasks, rather than globally optimizing for semantic understanding.
Data Source
AI summary
An electronic device for record linkage including a memory storing one or more instructions, and a processor that executes the one or more instructions to generate one or more vectors of one or more strings from a reference database based on a modified Levenshtein distance, and generate a vector database for spelling similarity based on the one or more vectors. The modified Levenshtein distance is based on one or more parameters, including at least one of: a first number of insertions, a second number of deletions, a third number of replacements, or a fourth number of matches, one or more of the one or more parameters including a predefined weight, and one or more fixed strings.