Context-Aware AI Framework Using Knowledge Graphs
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Solution Overview
Problem
Current AI frameworks lack the ability to develop context-based machine learning models, failing to link context with use cases, which hinders the generation of optimized machine learning models for specific problems.
Innovation Solution
A computer-implemented method and system for context-based machine learning model generation, which collects ground data from previous model deployments, generates a knowledge graph, determines initial hyperparameters, and modifies them to create a machine learning model tailored to new problems using cross-domain knowledge transfer and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AI frameworks perform machine learning operations without context, then the basic machine learning functions can be executed, but the ability to generate optimized models for specific problems is hindered
Solution Approach 1:
The patent implements a nested structure where a knowledge graph is embedded within the AI framework, which itself is nested within the broader machine learning pipeline. The knowledge graph contains nested taxonomies and use case contexts that are progressively refined during model generation, allowing context to be embedded at multiple levels of the system hierarchy.
Solution Approach 2:
The framework performs preliminary actions by collecting ground data from previous deployments and constructing a knowledge graph with taxonomies and use case contexts before actual model generation occurs. This pre-processing of contextual information enables faster and more targeted model optimization when specific problems are encountered.
2Reliability
If ground data from previous deployments is collected and processed, then context-aware model generation is enabled, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary data collection and knowledge graph construction during periods when models are not being actively generated. By pre-processing ground data from previous deployments and organizing it into structured taxonomies and use case contexts, the framework reduces the computational burden and time required during actual model generation operations.
Solution Approach 2:
The framework creates simplified copies of complex contextual information through the knowledge graph structure. Instead of processing raw ground data repeatedly, the system maintains structured representations (taxonomies, use case contexts, and relationships) that can be efficiently queried and applied during model generation, reducing computational overhead while preserving essential contextual information.
3Loss of information
If a knowledge graph is generated from ground data, then context linkage is achieved, but the complexity of data processing increases
Solution Approach 1:
The knowledge graph is segmented into distinct modular components including taxonomies, use case contexts, and relationship structures. Each segment handles a specific aspect of contextual information, allowing the system to process and manage complex data through organized, manageable units that can be independently constructed and queried.
Solution Approach 2:
The framework transforms flat ground data into a multi-dimensional knowledge graph structure by adding dimensions such as taxonomic hierarchies, use case contexts, and contextual relationships. This dimensional transformation organizes unstructured or semi-structured data into a rich, queryable format that preserves contextual information while enabling efficient processing through structured access patterns.
4Manufacturing precision
If hyperparameters are modified based on knowledge graph, then model performance for specific problems improves, but the complexity of parameter tuning increases
Solution Approach 1:
The knowledge graph serves as an intermediary between raw problem descriptions and hyperparameter generation. Instead of directly mapping problems to hyperparameters, the system uses the knowledge graph to translate problem contexts into structured queries that retrieve relevant hyperparameter settings from historical data, simplifying the tuning process while maintaining precision.
Solution Approach 2:
The framework copies and adapts hyperparameter settings from similar historical problems stored in the knowledge graph. By identifying problems with similar contextual characteristics through taxonomy matching and use case analysis, the system retrieves and modifies proven hyperparameter configurations rather than generating them from scratch, reducing complexity while maintaining optimization quality.
Data Source
AI summary
A method, system, and computer program product for context-based machine learning model generation are provided. The method collects ground data for a set of machine learning model deployments associated with a set of problems. A knowledge graph is generated for the set of machine learning models based on the ground data. An initial set of hyperparameters are determined for a new problem based on the knowledge graph. A modified set of hyperparameters are generated for the new problem based on the initial set of hyperparameters. The method generates a machine learning model for the new problem based on the modified set of hyperparameters.