Hybrid Knowledge Graph for Machine Learning Component Retrieval
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Solution Overview
Problem
Existing machine learning model creation and retrieval processes are inefficient due to lack of information about model locations and capabilities, making it difficult to find or generate models for specific tasks.
Innovation Solution
A hybrid knowledge representation system that structures machine learning tasks into a graph representation, allowing for the search, retrieval, and automatic creation of machine learning components, including models and datasets, by combining fragments based on user specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are stored in repositories without structured information, then storage is simple, but retrieval efficiency and model location capability deteriorate
Solution Approach 1:
The patent segments machine learning models into reusable fragments (e.g., data preprocessing modules, model architectures, training configurations) that can be independently stored, retrieved, and recombined. This segmentation enables efficient retrieval of specific components without requiring complete model matches, directly improving retrieval efficiency while organizing complexity through modular structure
Solution Approach 2:
The patent introduces a knowledge graph structure that adds semantic dimensions to model storage, organizing models not just by file location but by capabilities, tasks, and relationships. This dimensional transformation from flat storage to graph-based semantic organization enables efficient querying and retrieval while managing complexity through structured relationships
2Adaptability or versatility
If complete machine learning models are stored for every possible task, then model availability improves, but storage requirements and system complexity increase
Solution Approach 1:
The patent merges multiple model fragments that can be recombined to serve different tasks. Instead of storing complete models for every possible task combination, the system stores reusable fragments (data loaders, preprocessing pipelines, model layers, training configurations) that can be assembled into complete models on-demand, reducing storage requirements while maintaining versatility
Solution Approach 2:
The patent creates universal model fragments that can serve multiple functions across different tasks. For example, a generic convolutional layer or data preprocessing module can be reused across image classification, object detection, and segmentation tasks, enabling the system to provide broad model availability without storing task-specific complete models for every scenario
3Manufacturing precision
If machine learning models are manually created for each task, then model precision for specific tasks improves, but time consumption and labor requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and structuring model components into standardized, reusable fragments with defined interfaces and capabilities. Data preprocessing pipelines, model architectures, and training configurations are prepared in advance as modular units, enabling rapid assembly of task-specific models without manual creation from scratch, thus reducing model creation time while maintaining precision through proven components
Solution Approach 2:
The patent introduces an intermediary system (the knowledge graph and model assembly engine) that mediates between raw model fragments and task-specific requirements. This intermediary automatically matches available fragments with task specifications, selects appropriate components, and assembles complete models, eliminating the need for manual model creation while ensuring task-appropriate precision through intelligent component selection
4Measurement precision
If detailed information about model capabilities is stored, then model selection accuracy improves, but information processing complexity and storage overhead increase
Solution Approach 1:
The patent segments model information into discrete, structured attributes within the knowledge graph (e.g., input data types, output formats, computational requirements, task categories). This segmentation organizes detailed capability information into manageable, queryable units, improving model selection accuracy while managing information complexity through structured attribute decomposition
Solution Approach 2:
The patent transforms detailed model capability information into a multi-dimensional knowledge graph structure where capabilities are represented as nodes and relationships in a semantic network. This dimensional transformation organizes complex information about model capabilities, tasks, and relationships into a structured graph that enables precise querying and matching while managing information complexity through hierarchical and relational organization
Data Source
AI summary
A hybrid knowledge representation is searched for a machine learning component corresponding to a search query. The hybrid knowledge representation may be structured as nodes representing machine learning workflow components and edges (e.g., links) connecting the nodes based on relationships between the nodes. Responsive to finding the machine learning component in the hybrid knowledge representation, the machine learning component is returned. Responsive to not finding the machine learning component in the hybrid knowledge representation, the hybrid knowledge representation is searched for machine learning model fragments associated with building the machine learning component, generating a new machine learning component by combining the machine learning model fragments and returning the new machine learning component.