Entity Resolution Cascade Using LLM Prompts and Neural Matching
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Solution Overview
Problem
Computing systems face inefficiencies and increased resource consumption when processing large amounts of data with duplicate records, leading to increased computing requirements and reduced computational speed and efficiency.
Innovation Solution
A computing system utilizing a multi-stage cascade of domain-agnostic large language models and downstream neural network classifiers to identify and merge matching data records, combined with approximator networks for increased speed and efficiency, and a platform that supports seamless integration and management of entities, relationships, and interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computing systems process large amounts of disparate data records, then comprehensive data coverage is achieved, but computing resource consumption increases and processing efficiency decreases
Solution Approach 1:
The system performs preliminary entity resolution processing to identify and merge duplicate records before main data processing operations. By pre-processing the data to eliminate duplicates, the system reduces the volume of data that requires subsequent processing, thereby improving overall processing efficiency while maintaining comprehensive data coverage.
Solution Approach 2:
The system extracts and removes duplicate data records from the dataset through entity resolution techniques. By identifying records that represent the same entity and removing redundant copies, the system reduces data volume and processing requirements while preserving all unique information.
2Reliability
If computing systems process all data records including duplicates, then complete data analysis is achieved, but computing resource requirements increase
Solution Approach 1:
The system performs preliminary entity resolution to identify and consolidate duplicate records before main processing operations. This pre-processing step ensures that all unique entities are captured while eliminating redundant processing of duplicate records, thereby maintaining data completeness while reducing computing resource consumption.
Solution Approach 2:
The system creates a consolidated representation of duplicate records through entity resolution, where multiple records representing the same entity are merged into a single representative record. This approach preserves the information from all original records while reducing the computational burden of processing each duplicate individually.
3Speed
If traditional entity resolution methods are used, then processing speed is maintained, but accuracy of matching data records decreases
Solution Approach 1:
The system segments the entity resolution process into multiple stages: generating candidate matches using rules-based methods for speed, then applying machine learning models to refine and validate matches for accuracy. This multi-stage approach allows the system to maintain high processing speed in the initial filtering phase while achieving high matching accuracy in the final validation phase.
Solution Approach 2:
The system introduces machine learning models as an intermediary between initial rule-based matching and final match confirmation. The ML models act as a refinement layer that takes candidate matches generated by fast rules-based methods and improves their accuracy through learned patterns, thereby achieving both speed and precision.
4Productivity
If simple matching rules are used, then processing efficiency is maintained, but accuracy of identifying matching records decreases
Solution Approach 1:
The system segments match identification into two phases: first using simple, efficient rules to generate candidate matches quickly, then applying more sophisticated machine learning models to accurately validate and refine these candidates. This segmentation allows the system to maintain high processing efficiency in the candidate generation phase while achieving high accuracy in the validation phase.
Solution Approach 2:
The system introduces machine learning models as an intermediary validation layer that processes the output of simple matching rules. The ML models refine the candidate matches by applying learned patterns and context, thereby improving match identification accuracy while building upon the efficient candidate generation provided by the simple rules.
Data Source
AI summary
Among other techniques, techniques for machine learning-based entity resolution are described. An example method includes receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity; identifying a plurality of first attributes in a data model; identifying a plurality of second attributes in the data model; creating a first string based on the plurality of first attributes of the data model; creating a second string based on the plurality of second attributes of the data model; generating a first prompt based on the first string; generating a second prompt based on the second string; providing the first prompt to a domain-agnostic large language model; generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result; clipping the first domain-agnostic large language model result; providing the second prompt to the domain-agnostic large language model; generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result; clipping the second domain-agnostic large language model result; generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model result; and merging, based on the machine learning final result, the first entity and the second entity.