Automated Mathematical Ontology Extraction System
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Solution Overview
Problem
Current systems lack the capability to efficiently and cost-effectively manage and store the vast amount of granular mathematical concepts across various grade levels, leading to inefficiencies in data management and redundancy in manual extraction processes, which hinders the development of a comprehensive ontology of mathematics.
Innovation Solution
The Ontology Editor System, a computer-based system with extensive database storage and manipulation capabilities, automates the extraction and organization of mathematical concepts, utilizing Agile/Serum management principles and data-mining algorithms to minimize redundancies and optimize the extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction processes are used to build mathematical ontology, then human analysts can perform detailed and rigorous concept extraction, but the process becomes extremely time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical extraction processes with an automated computer-based system that uses natural language processing, machine learning, and data mining algorithms to extract mathematical concepts from text, thereby eliminating the time and cost constraints of manual analysis while maintaining extraction quality
Solution Approach 2:
The patent introduces an intermediary automated extraction system that acts as a bridge between raw mathematical text and structured ontology, using computational tools to process and organize concepts without requiring direct human intervention in the extraction process
2Productivity
If multiple analysts work in parallel to extract concepts, then productivity increases, but coordination difficulty and redundancy control become exponentially more complex
Solution Approach 1:
The patent merges multiple extraction operations into a single unified automated system, combining the capabilities of multiple analysts into one coordinated platform that processes concepts systematically without the coordination overhead of managing multiple human workers
Solution Approach 2:
The automated extraction system performs self-coordination and self-management, automatically handling concept identification, classification, and deduplication without requiring external coordination mechanisms, thereby simplifying the management of parallel processing operations
3Quantity of substance
If comprehensive mathematical concepts are extracted across all grade levels, then the ontology becomes more complete and useful, but data storage and management requirements become overwhelming
Solution Approach 1:
The patent segments the comprehensive mathematical ontology into organized hierarchical structures by grade level, subject area, and concept type, allowing the system to manage and retrieve vast amounts of mathematical concepts through structured categorization that simplifies data management while maintaining completeness
Solution Approach 2:
The patent creates a universal data management framework that handles diverse mathematical concepts across all grade levels through a single standardized system, enabling the ontology to scale comprehensively without proportionally increasing management complexity through automated organization and retrieval mechanisms
Data Source
AI summary
A method of extracting mathematical concepts from mathematical exercise representations includes the steps of accessing at least one document including a plurality of mathematical exercise representations, wherein each mathematical exercise representation comprises one or more algorithmic, linguistic, geometric, and graphic mathematical exercise representations; extracting each mathematical exercise representation to identify one or more mathematical concepts; identifying a relationship between a first mathematical concept and a second mathematical concept to identify a plurality of concept groups; populate an optimization table to associate at least one of the mathematical concepts and concept groups with one or more of the plurality of mathematical exercise representations; and optimize the optimization table to remove redundancies.