Entity Resolution System for Fused Person Views
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Solution Overview
Problem
Entity resolution algorithms face challenges in consolidating records from disparate sources, often resulting in data loss due to discrepancies in field names and formats, leading to incomplete fused views of individuals.
Innovation Solution
A method involving an entity resolution and relational intelligence computer system that extracts records from multiple data sources, identifies matches based on features, merges relevant information, and generates a fused view for each individual, incorporating rules and algorithms to handle variations and relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If records from multiple disparate data sources are combined into a single record, then the completeness of the fused view is improved, but data loss occurs due to discrepancies in field names and formats
Solution Approach 1:
The patent introduces an intermediary entity resolution system that mediates between disparate data sources and the fused view. This system uses matching algorithms to identify corresponding records across sources and transformation logic to reconcile field name and format discrepancies, preventing data loss while managing complexity through a dedicated intermediate processing layer.
Solution Approach 2:
The patent transforms records by changing their parameters (field names, formats, data types) to a standardized schema during the entity resolution process. This parameter transformation allows records from different sources with varying field structures to be successfully merged without data loss, as each source's parameters are adapted to match the target fused view structure.
2Measurement precision
If user-defined functions are used to compare fields for matching, then the accuracy of match determination is improved, but data loss occurs when combining associated fields
Solution Approach 1:
The patent merges associated fields from matched records by combining their values rather than replacing one with the other. This merging approach preserves information from all source records while maintaining the accuracy of match determination achieved through user-defined comparison functions, directly addressing the data loss problem in field combination.
Solution Approach 2:
The entity resolution system acts as an intermediary that receives matched records from user-defined functions and applies merging logic to their associated fields. This intermediate processing step ensures that accurate matches are maintained while field values are combined in a way that preserves all available information rather than losing data during the combination process.
3Ease of operation
If records are consolidated from multiple sources into a single record, then the unified view of the person is improved, but discrepancies in field names and formats between sources create processing challenges
Solution Approach 1:
The patent implements a universal entity resolution framework that can handle multiple data sources with different field structures through a common processing architecture. This universal system provides multi-functionality by accommodating various field names and formats from different sources while producing a standardized unified view, reducing processing complexity through a single versatile solution rather than source-specific processing.
Solution Approach 2:
The patent applies parameter transformation to convert field names and formats from various data sources into a standardized schema during consolidation. This parameter change process automatically handles discrepancies in field structures, enabling easy creation of unified views while the system manages the processing complexity of field reconciliation through systematic transformation rules.
Data Source
AI summary
In accordance with particular embodiments, a method includes receiving data from disparate data sources comprising data associated with different people. At least one of the data sources comprises data associated with at least one common person. The method also includes extracting at least one record from each of the disparate data sources. Each extracted record is associated with a corresponding person and comprises at least one feature associated with the corresponding person. The method additionally includes distributing the records to computers configured to identify matches between the records based on at least one matching feature associated with two or more records. The method further includes merging the at least one feature from each of the records of each corresponding match into merged records, each merged record corresponding to a different match. The method also includes generating at least one fused view associated with the at least one common person and comprising a merged record.