Entity Resolution Language Model Pre-Training With Contrastive Learning
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Solution Overview
Problem
Entity record resolution in large databases is challenging due to the lack of standardization, multiple sources, and errors in entity records, leading to inefficiencies in deduplication, linkage, and canonicalization, and requiring laborious manual rule updates.
Innovation Solution
A self-contrastive machine learning model with contrastive loss optimization is used to augment entity records and determine positive and negative contrasts, eliminating the need for manual updates and improving entity resolution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual rule updates are used for entity resolution, then the system can handle non-standardized data, but the maintenance complexity and time consumption increase significantly
Solution Approach 1:
The system employs self-contrastive learning where the model automatically generates positive and negative contrast pairs from entity records and performs self-training without manual rule updates. The contrastive loss function enables the model to learn discriminative features autonomously, eliminating the need for continuous manual maintenance of resolution rules.
Solution Approach 2:
The patent transforms the entity resolution problem from rule-based parameter matching to learned parameter representations. By using contrastive loss optimization, the model learns optimal parameter configurations that automatically adapt to non-standardized data patterns, replacing manual rule parameter updates with automated parameter learning.
2Measurement precision
If traditional entity resolution methods are used, then processing accuracy can be maintained, but processing time and computational resources increase
Solution Approach 1:
The system performs pre-training of the language model on entity records before actual resolution tasks. This preliminary contrastive learning phase enables the model to learn discriminative patterns in advance, allowing faster and more accurate entity resolution during inference without requiring complex real-time processing.
Solution Approach 2:
The patent replaces traditional mechanical rule-based entity resolution systems with a machine learning-based semantic understanding system. The contrastive loss optimization enables the model to automatically capture semantic similarities and differences, substituting manual rule processing with intelligent automated decision-making that is both faster and more accurate.
3Loss of information
If extensive data storage is used to maintain entity records, then data completeness is improved, but storage costs and processing overhead increase
Solution Approach 1:
The system extracts only the essential discriminative features from entity records through contrastive learning, rather than storing and processing all raw data. The pre-trained language model learns to represent entities in compressed vector spaces that capture the most important distinguishing characteristics, reducing storage requirements while maintaining resolution accuracy.
Data Source
AI summary
In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a dataset of entity records, identifying, a candidate entity record of the plurality of entity records, utilizing a set of predefined rules to generate a first augmented record, and a second augmented record, utilizing, at least one contrastive loss optimization functions to train parameters of an unsupervised self-contrastive machine learning language model to distinguish between similar entity records representing a same entity and dissimilar entity records.