Attribute-Based Data Matching with Multi-Modal Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data matching systems rely on keyword-based searches, which can produce inaccurate results if keywords are erroneous or missing, and fail to consider partial rankings and attribute weights effectively, leading to biased data and suboptimal matches across various domains.
Innovation Solution
An attribute-based data matching system that generates and aggregates attributes using multiple models (raw, rule-based, classification-based, and deep learning-based) with multi-level weights and distance metrics, enabling partial ranking aggregation and user feedback integration to improve match relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based search is used for data matching, then the system is simple to implement, but the match accuracy deteriorates when keywords are erroneous or missing
Solution Approach 1:
The system transforms the matching approach by changing the fundamental parameter from keyword-based string matching to attribute-based multi-modal matching. Multiple attributes (name, email, phone, address, etc.) are extracted and compared with different distance metrics appropriate for each attribute type, significantly improving match accuracy while maintaining reasonable system complexity
Solution Approach 2:
The system combines multiple matching modalities (different attribute types and distance metrics) into a composite matching framework. Each attribute is matched using its optimal distance metric (e.g., Levenshtein for text, cosine similarity for vectors), and results are aggregated to produce a comprehensive match score, achieving high accuracy through diversified matching strategies
2Speed
If traditional data matching is used, then the processing speed is fast, but the reliability of matches deteriorates due to inability to handle partial rankings and attribute weights
Solution Approach 1:
The system segments the matching process into distinct stages: attribute extraction, pairwise scoring with appropriate distance metrics, partial ranking generation for each attribute, and aggregated ranking computation. This segmentation allows each stage to be optimized independently, maintaining speed while improving reliability through careful handling of partial rankings and attribute weights
Solution Approach 2:
The system introduces dynamic weighting of attributes and adaptive handling of partial rankings. Attributes can be weighted based on their importance and reliability, and the system dynamically adjusts how partial rankings are aggregated, improving match reliability without significantly impacting processing speed
3Measurement precision
If multiple attributes and models are used for matching, then the match accuracy is improved, but the system complexity increases
Solution Approach 1:
The system employs a universal framework that handles multiple attribute types (text, numerical, categorical, vector) and multiple distance metrics within a single unified matching engine. This multi-functional approach improves match accuracy across diverse data types while avoiding the need for separate specialized systems, thereby controlling overall complexity
Solution Approach 2:
The system introduces an intermediary attribute extraction and normalization layer that transforms diverse input data into standardized attribute representations. This intermediary layer simplifies the subsequent matching process by providing consistent, pre-processed attributes, reducing the complexity of the core matching algorithm while maintaining high accuracy
4Loss of energy
If keyword-based matching is used, then the computational resources are conserved, but the adaptability to different domains deteriorates
Solution Approach 1:
The system changes the matching parameters to be domain-agnostic by using generic attribute types and distance metrics that can be applied across different domains. The same framework handles personal information matching, product matching, and record linkage by simply changing the attribute definitions and corresponding distance metrics, achieving high domain adaptability without proportionally increasing computational resources
Data Source
AI summary
A attribute-based data matching system determines top matches for a first element from a plurality of second elements. The data matching system extracts attributes for the first element from first dataset and attributes for the plurality of second elements from a plurality of second datasets. Pairs of attributes are generated wherein each attribute pair includes an attribute of the first element and an attribute of one of the plurality of second elements. Pairwise rankings of the plurality of second elements corresponding to the attributes of the first element are generated based on weights of the attribute pairs. The pairwise rankings of the attribute pairs are aggregated to determine a ranked list that orders the plurality of second elements based on the extent of their match with the first element. User feedback to the ranked list can be collected and used to adjust the data matching system.