Dynamic Rule-Based Matching Engine for Fuzzy Entity Resolution
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Solution Overview
Problem
Traditional computer matching engines are inflexible and rely on predefined rules, struggling to handle inputs in unstructured forms and dynamically changing rules, which limits their ability to identify matches in situations where elements are not precisely defined.
Innovation Solution
A dynamic rule-based matching system that uses a processor to receive and modify rules in real-time, allowing for fuzzy matching and prioritization based on multiple rules, with a distributed computing grid and machine learning algorithms to scale and adapt to varying inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined rules are used for matching, then matching accuracy is improved, but system flexibility and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic rule modification capability that allows rules to be changed at runtime without requiring system restart or reconfiguration. The rule engine can add, remove, or modify matching rules dynamically based on changing requirements, transforming a static rule-based system into a dynamic one that maintains both precision and adaptability.
Solution Approach 2:
The system allows modification of rule parameters such as matching thresholds, weights, and criteria dynamically. By changing these parameters at runtime, the system can adapt to different matching scenarios while maintaining the structured rule-based approach that ensures accuracy.
2Productivity
If rigid element formation is used for parsing, then parsing efficiency is improved, but ability to handle unstructured inputs deteriorates
Solution Approach 1:
The patent introduces dynamic schema generation that creates parsing structures adaptively based on the input data characteristics. Rather than requiring fixed rigid schemas beforehand, the system can generate appropriate parsing structures dynamically, allowing efficient processing of both structured and unstructured inputs.
Solution Approach 2:
The system implements a universal parsing framework that can handle multiple input formats (structured, semi-structured, unstructured) through a common dynamic schema generation mechanism. This multi-functional approach maintains parsing efficiency across different input types without requiring separate rigid parsing logic for each format.
3Measurement precision
If substantial similarity threshold is required for matching, then match quality is improved, but number of identified matches deteriorates
Solution Approach 1:
The patent implements weighted rule evaluation where different matching rules have different weights based on their importance and reliability. Instead of applying a single uniform similarity threshold, the system evaluates matches against multiple rules with varying weights, allowing high-quality matches to be identified with appropriate precision while capturing more potential matches through the weighted aggregation of multiple rule assessments.
Data Source
AI summary
A system and related method are provided for dynamically modifying a rule-based matching system. The method comprises using a processor for receiving a plurality of rules that are stored in a matching metadata database in a storage device, and receiving a plurality of entities as inputs. The method further comprises determining a degree of matching between a first entity and a second entity of the plurality of entities, using at least two of the plurality of rules that contribute to the degree of matching. The method then outputs the determined degree of matching to a display device, and dynamically modifies, at run-time, at least one of the plurality of rules.