Ontology-Based Modeling Framework for Strategic Analysis
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Solution Overview
Problem
Strategic planners and policy makers face limited analysis capabilities in addressing complex economic, resource, and financial challenges due to the breadth of problem sets and the lack of a single model or tool that can effectively cover the entire problem space, leading to decisions based on intuition rather than hard mathematical analysis.
Innovation Solution
A modeling and simulation framework that automates the selection of data, models, and visualization outputs using an ontology-based system, where a reasoner component selects relevant data sets and models based on a challenge specification, executes the selected models, and provides confidence indications and visualized results, enabling insight into a wide range of scenarios without prior knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model or tool is used to cover the problem space, then device complexity is reduced, but the ability to provide comprehensive analysis across all economic, resource, and financial areas deteriorates
Solution Approach 1:
The system segments the comprehensive problem space into distinct domains (economic, resource, financial warfare) with specialized models for each. The ontology framework divides data into structured categories, allowing each segment to be analyzed by appropriate specialized models while maintaining overall system integration through the unified ontology structure.
Solution Approach 2:
The ontology-based framework serves as a universal structure that can accommodate multiple specialized models across different domains. The system provides multi-functionality by enabling a single platform to handle diverse analysis types (trade analysis, resource conflict, financial warfare) through a common ontology interface that adapts to different problem types.
2Reliability
If multiple specialized models and data repositories are used to cover all problem areas, then analysis capability is improved, but device complexity and difficulty of operation increase
Solution Approach 1:
The ontology framework acts as an intermediary layer between the user and the complex collection of specialized models and data repositories. It provides a standardized interface that translates user queries into appropriate model selections and data retrievals, shielding users from the underlying system complexity while maintaining access to comprehensive analysis capabilities.
Solution Approach 2:
The system incorporates feedback mechanisms where the ontology structure learns from analysis results and user interactions. Confidence indications provide feedback on model reliability, and the system adapts model selections based on previous analysis outcomes, improving reliability while managing complexity through iterative refinement.
3Measurement precision
If comprehensive data from multiple repositories is integrated, then measurement precision and analysis quality improve, but loss of time for data integration and processing increases
Solution Approach 1:
The system performs preliminary actions by pre-structuring data according to the ontology framework before analysis is needed. Data from multiple repositories is pre-integrated and tagged with ontology descriptors, so when a query is made, the system can quickly retrieve relevant pre-processed data without performing time-consuming integration at query time.
Solution Approach 2:
The system replaces manual data integration mechanics with automated ontology-based retrieval. Instead of manually combining data from multiple repositories, the ontology framework automatically identifies and retrieves relevant data through structured queries, substituting automated information retrieval for manual data integration processes.
4Productivity
If automated model selection and execution is implemented, then productivity and speed of analysis improve, but device complexity and automation extent increase
Solution Approach 1:
The system implements self-service through automated model selection and execution. The ontology framework automatically determines which models and data are appropriate for a given query without human intervention, selecting and executing models based on the problem type and data availability, thereby improving productivity while managing complexity through autonomous operation.
Data Source
AI summary
A computer modeling and simulation framework for analysis automates selection of data, models and visualization outputs for providing an insight into a wide range of scenarios without a priori knowledge of the scenarios. The framework organizes the data and models through an ontology for the data and models. A challenge specification is entered by an analyst via a structured document or natural language input, wherein a reasoner processing component of the system selects relevant data sets and models based on the challenge specification and the data and model ontology. The system may provide confidence indications based on the quality of relevant models and data and executes the selected models using the selected data sets as inputs. Results are stored and visualized resulting data presented for the analyst. A visualization ontology enables the system to select an optimum visualization approach.