String Transformation Rules for Semantic Record Matching
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Solution Overview
Problem
Traditional string similarity functions fail to account for synonyms, abbreviations, and semantic equivalencies in a flexible manner, making it challenging to match semantically equivalent data records due to large numbers of possible string transformations.
Innovation Solution
The techniques involve learning string transformation rules from sample matched pairs of strings, using a greedy approximation algorithm to identify a concise set of syntactic rules that can explain most differences between matching strings, thereby enabling effective record matching across varying representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional string similarity functions are used, then the matching process is simple, but they fail to account for synonyms, abbreviations, and semantic equivalencies
Solution Approach 1:
The system performs self-learning by automatically generating transformation rules from example matched string pairs provided by the user, eliminating the need for manual rule compilation. The learning module analyzes the example pairs and autonomously derives the transformation rules that capture semantic equivalencies specific to the domain.
Solution Approach 2:
The system performs preliminary learning by generating transformation rules in advance from example matched pairs before the actual record matching task. This pre-computed rule set is then reused for matching, improving efficiency and consistency across multiple matching operations.
2Measurement precision
If a comprehensive set of transformation rules is manually compiled, then matching accuracy improves, but the time and effort required increases significantly
Solution Approach 1:
The system performs self-learning by automatically generating transformation rules from example matched string pairs provided by the user, eliminating the need for manual rule compilation. The learning module analyzes the example pairs and autonomously derives the transformation rules that capture semantic equivalencies specific to the domain.
Solution Approach 2:
The patent replaces the manual mechanical process of rule compilation with an automated learning system. The learning module uses computational algorithms to analyze example matched pairs and generate transformation rules, substituting human effort with automated intelligence.
3Adaptability or versatility
If many possible string transformations are considered, then matching coverage increases, but the computational complexity increases
Solution Approach 1:
The learning module extracts only the relevant transformation rules from the example matched pairs, filtering out unnecessary transformations. This selective extraction process identifies and retains only those rules that actually explain the observed string variations in the training data.
Solution Approach 2:
The system learns a concise subset of transformation rules that covers the majority of matching cases observed in the example pairs, rather than attempting to enumerate all possible transformations. This partial approach achieves sufficient coverage with reduced complexity.
Data Source
AI summary
Techniques are described to leverage a set of sample or example matched pairs of strings to learn string transformation rules, which may be used to match data records that are semantically equivalent. In one embodiment, matched pairs of input strings are accessed. For a set of matched pairs, a set of one or more string transformation rules are learned. A transformation rule may include two strings determined to be semantically equivalent. The transformation rules are used to determine whether a first and second string match each other.