Multimodal Learning for Entity Record Relationship Analysis
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Solution Overview
Problem
Existing methods struggle to accurately resolve ambiguities in entity information across different sources, particularly in large-scale business and medical applications, due to varying descriptions of the same or distinct entities, leading to customer confusion and reduced sales.
Innovation Solution
A multi-modal learning approach that combines analysis of text and non-text attributes, using machine learning models to generate feature sets from both text and non-text attributes, such as images, to detect similarities and relationships between entity records, enabling precise identification of duplicates and improving data organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If standardized approaches are used to store entity information, then data organization is improved, but adaptability to different vendor formats deteriorates
Solution Approach 1:
The system segments entity information into multiple attribute types (text attributes and non-text attributes) and processes each segment separately through specialized analysis sub-models. This allows the system to maintain standardized processing while accommodating diverse input formats from different vendors, resolving the contradiction between data organization stability and format adaptability.
Solution Approach 2:
The system changes parameters by transforming diverse vendor formats into standardized feature representations through machine learning models. By converting various text and non-text attributes into unified feature sets, the system achieves both adaptability to different formats and standardized data organization internally.
2Adaptability or versatility
If multiple different details pages are provided for the same product from different suppliers, then vendor diversity is maintained, but customer confusion increases
Solution Approach 1:
The system generates relationship scores that provide feedback on the similarity between entity records from different vendors. This feedback mechanism enables the system to identify and flag potential duplicates or highly similar products, allowing organizations to consolidate information presentation and reduce customer confusion while maintaining vendor diversity in the underlying data.
Solution Approach 2:
The multi-modal machine learning model serves multiple functions: it analyzes both text and non-text attributes, detects various types of relationships (similarity, duplication, inclusion), and works across different vendor formats. This universal approach enables consistent handling of diverse vendor information, improving customer understanding without sacrificing vendor diversity.
3Speed
If conventional text-only matching is used to identify duplicate records, then processing speed is maintained, but measurement precision of similarity deteriorates
Solution Approach 1:
The system transitions from text-only matching to multi-dimensional analysis by incorporating both text attributes and non-text attributes (such as images, videos, or other media). This dimensional expansion enables more precise similarity detection through the relationship score generator, which综合考虑 multiple attribute types to determine entity relationships, significantly improving measurement precision while maintaining acceptable processing speeds through efficient feature generation and model architecture.
Data Source
AI summary
Respective text feature sets and non-text feature sets are generated corresponding to individual pairs of a plurality of record pairs. At least one text feature is based on whether a text token exists in both records of a pair. Perceptual hash values are used for non-text feature sets. A machine learning model is trained, using the text and non-text feature sets, to generate relationship scores for record pairs. The model includes a text sub-model and a non-text sub-model.