Multi-purpose Data Management via Contrastive Learning
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Solution Overview
Problem
Existing natural language processing systems are limited to specific tasks and require large labeled datasets for training, making them inefficient for multi-purpose data management tasks and costly to adjust for changes in data format or sources.
Innovation Solution
A system that uses a contrastive learning technique to determine representative entity pairs in a shared space, allowing for data management tasks like integration, cleanup, and discovery without manual labeling, by training machine learning models using unlabeled data and existing transformer models like BERT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing natural language processing systems are used for specific tasks, then task performance is improved, but system versatility and adaptability to different data management tasks deteriorate
Solution Approach 1:
The patent applies universality by developing a single NLP system framework that can perform multiple data management tasks including entity matching, data integration, data cleanup, and data discovery. The system uses a unified architecture with contrastive learning that adapts to different tasks without requiring separate specialized systems, thereby achieving multi-functionality while maintaining task performance.
Solution Approach 2:
The patent implements dynamics through the use of contrastive learning that dynamically adapts to different data management tasks. The system can adjust its learning objectives and parameters based on the specific task requirements, allowing it to transition between different functions while maintaining optimal performance for each task.
2Measurement precision
If large labeled datasets are used for training, then model accuracy is improved, but training cost and time consumption deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training the model using contrastive learning on unlabeled data before fine-tuning on task-specific labeled data. This pre-training phase establishes useful representations that reduce the amount of labeled data needed and accelerate subsequent training, thereby reducing both training time and resource requirements while maintaining accuracy.
Solution Approach 2:
The system implements self-service through contrastive learning that automatically learns meaningful representations from unlabeled data without requiring manual annotation. The model serves itself by generating its own training signals through the contrastive objective, eliminating the need for expensive and time-consuming manual labeling while still achieving high accuracy.
3Measurement precision
If custom NLP systems are developed for each task, then task-specific performance is improved, but system complexity and maintenance cost deteriorate
Solution Approach 1:
The patent reduces system complexity by implementing a universal NLP framework that handles multiple data management tasks through a single architecture. This unified system eliminates the need to develop and maintain separate custom systems for each task, reducing overall complexity while preserving task-specific performance through adaptable learning objectives.
Solution Approach 2:
The patent combines multiple task-specific systems into a single unified framework by merging the entity matching, data integration, cleanup, and discovery functionalities into one system. This consolidation reduces maintenance costs and simplifies the overall system architecture while maintaining the ability to perform each specific task effectively.
Data Source
AI summary
Disclosed embodiments relate to data management of entity pairs. Techniques can include receiving at least two sets of data and a data management task request with each including a set of entities. Techniques can determine a location of each entity in received data sets in a representative space by determining representative structure of the set of entities. Techniques can then for an entity, a set of representative entity pairs from each set of the at least two sets of data based on how close they are in the representative space. Technique can then analyze the set of representative entity pairs to identify most similar entity pairs include in a set of candidate pairs by determining closeness of location of entities in each entity pair in the representative space. Technique can then determine matched entity pairs of the candidate pairs using a first machine learning model is trained using the candidate pairs by applying labels, and utilizing the matched pairs to perform the requested data management task.