Plug-and-Analyze Framework for Scalable Knowledge Base Updates
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Solution Overview
Problem
Conventional systems for generating knowledge bases lack flexibility, scalability, and adaptability, failing to accommodate changes in machine-learning models, data, or technology, and are unable to seamlessly update, add, or remove models and files.
Innovation Solution
A plug-and-analyze framework that uses a server to trigger execution of machine-learning models to extract and refine data, allowing for flexible, scalable, and adaptable knowledge base generation, with features like resuming models at failure points and iteratively updating the knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are used for generating knowledge bases, then the system structure is simple, but the system lacks flexibility, scalability, and adaptability
Solution Approach 1:
The system is divided into independent plug-compatible modules including data extraction modules, data processing modules, and knowledge base generation modules. Each module can be independently added, removed, or updated without affecting the entire system, thereby achieving flexibility while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system employs universal interfaces and standardized protocols that allow different types of machine learning models and data sources to be integrated through a common framework. This multi-functionality enables the system to handle diverse data formats and processing requirements without requiring complete system redesign.
2Adaptability or versatility
If conventional systems are used for generating knowledge bases, then the initial setup is straightforward, but the system cannot seamlessly update, add, or remove models and files
Solution Approach 1:
The system implements dynamic configuration capabilities where machine learning models and data files can be added, removed, or modified at runtime without system downtime. The plug-compatible architecture allows dynamic registration and deregistration of modules, enabling seamless updates while maintaining ease of operation through automated dependency management.
Solution Approach 2:
The system includes automated feedback mechanisms that monitor model performance and data quality, triggering automatic updates or retraining when improvements are detected. This feedback loop enables the system to adaptively update components based on performance metrics while maintaining operational simplicity through automated decision-making.
3Productivity
If multiple machine-learning models are used to extract and refine data, then the data processing capability is enhanced, but the system complexity increases
Solution Approach 1:
Multiple machine learning models are organized into segmented, plug-compatible modules with clearly defined input-output interfaces. Each model handles a specific aspect of data extraction or refinement, and their modular structure allows them to be managed independently, reducing the operational complexity despite having multiple models working in parallel.
Solution Approach 2:
Standardized interface layers and data transformation modules act as intermediaries between different machine learning models, abstracting the complexity of model interactions. These intermediary components handle data format conversions, parameter mappings, and coordination logic, thereby enabling multiple models to work together without directly increasing system complexity.
Data Source
AI summary
Provided herein are system, apparatus, device, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a knowledge base. In a given embodiment, machine-learning techniques and models are used to extract information and knowledge from different document formats by processing any supported unstructured, semi-structured and structured data types. The extracted information and knowledge may be used to generate a knowledge base.