Knowledge Graph Optimization Configuration
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Solution Overview
Problem
Current methods face challenges in configuring complex optimization scenarios from sensor lists using customized use case constraints, leading to scalability issues and the need for either highly specialized or generalized optimization solutions that do not fully address practical needs.
Innovation Solution
The solution employs a semantic knowledge graph to encode sensors and system components, along with their causal relationships, and applies graph patterns to select entities for optimization problems, using atomic optimization templates and external solvers to execute and explain the optimization results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current methods are used to configure complex optimization scenarios from sensor lists with customized use case constraints, then optimization problems can be solved, but scalability issues arise and the solutions are either highly specialized or overly generalized
Solution Approach 1:
The patent segments the optimization configuration process into distinct components: a knowledge graph that encodes domain knowledge and constraints, graph patterns that represent optimization scenarios, and atomic optimization templates that define solution structures. This segmentation allows complex optimization problems to be broken down into manageable, reusable components that can be assembled systematically, resolving the contradiction between adaptability to custom constraints and configuration complexity.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary layer between sensor lists and optimization solvers. This knowledge graph encodes domain knowledge, constraints, and relationships in a structured format, serving as a mediator that translates customized use case constraints into formal optimization problems. This intermediary resolves the contradiction by providing a systematic translation mechanism that maintains adaptability while reducing configuration complexity.
2Reliability
If highly specialized optimization solutions are developed for specific use cases, then they address practical needs accurately, but they lack scalability to other scenarios
Solution Approach 1:
The patent creates universal graph patterns and atomic optimization templates that can represent multiple optimization scenarios across different domains. These templates encode general optimization logic and constraints that can be instantiated with specific domain knowledge from the knowledge graph, enabling the same framework to handle diverse scenarios from sensor lists while maintaining accuracy for each specific use case. This universality resolves the contradiction between reliability for specific cases and scalability to other scenarios.
Solution Approach 2:
The patent performs preliminary encoding of domain knowledge, constraints, and optimization patterns in the knowledge graph before actual optimization execution. By pre-structuring the problem space with graph patterns and atomic templates that capture essential relationships and constraints, the system prepares reusable components that can be quickly instantiated for different scenarios. This preliminary action ensures accuracy for specific use cases while enabling rapid adaptation to new scenarios, resolving the reliability-scalability contradiction.
3Adaptability or versatility
If generalized optimization solutions are used to achieve scalability, then they can handle multiple scenarios, but they fail to fully address practical needs with custom constraints
Solution Approach 1:
The patent applies local quality by allowing different parts of the knowledge graph to encode domain-specific constraints and relationships tailored to particular scenarios. While the overall framework uses universal graph patterns and atomic templates for scalability, each local region of the knowledge graph can be customized with specific domain knowledge, constraints, and entities. This local customization ensures that generalized solutions accurately capture the nuances of custom constraints for each specific application, resolving the contradiction between scalability and accuracy.
Solution Approach 2:
The patent enables parameter changes by allowing the knowledge graph to be dynamically populated and modified with domain-specific parameters and constraints for different scenarios. The graph patterns and atomic optimization templates maintain their general structure for scalability, while the parameters and instance data can be changed to reflect specific use case requirements. This flexibility in parameter changes allows the system to scale across scenarios while maintaining accuracy for custom constraints in each domain.
Data Source
AI summary
Various embodiments are provided for configuring optimization problems from one or more sources in a computing environment by a processor. A knowledge graph may be generated from a knowledge domain and one or more data sources. One or more graph pattens may be applied to match one or more entities in the knowledge graph with one or more atomic optimization templates. An optimization problem configured from the one or more atomic optimization templates and a plurality of data may be executed.