Control Unit for Taxonomy Element Mapping
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Solution Overview
Problem
Mapping the source taxonomy to the target taxonomy in shared instructional documents across organizations is a tedious and costly process due to regulatory and corporate merger complexities.
Innovation Solution
A control unit identifies semantic bridges between documents using string-based similarities, generates taxonomy graphs and graph embeddings, and maps elements using a vector function to correlate and match elements across multiple documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual taxonomy mapping is performed between source and target organizations, then mapping accuracy can be maintained, but the process becomes tedious and costly
Solution Approach 1:
The patent replaces manual taxonomy mapping (mechanical human effort) with an automated computational system that uses string-based similarity algorithms, graph embeddings, and vector functions to perform element matching between source and target taxonomies, thereby reducing time and cost while maintaining accuracy
Solution Approach 2:
The patent introduces an intermediary control unit that acts as a mediator between source and target taxonomies, using semantic bridge identification and graph embedding techniques to facilitate automated element mapping without direct manual intervention
2Loss of time
If automated mapping methods are used, then time and cost are reduced, but mapping precision may deteriorate
Solution Approach 1:
The patent transforms the mapping problem from traditional string matching into a multi-dimensional graph embedding space where elements are represented as nodes and relationships as edges, enabling automated processing while preserving semantic relationships and improving matching accuracy through vector-based comparisons
Solution Approach 2:
The patent changes the parameter space for element comparison by using string-based similarity metrics, graph embedding vectors, and correlation coefficients instead of exact string matching, allowing automated systems to achieve high precision by measuring semantic similarity in transformed parameter spaces
3Device complexity
If traditional element matching is performed across multiple documents, then simplicity is maintained, but the ability to handle complex taxonomy relationships is limited
Solution Approach 1:
The patent segments the taxonomy mapping problem into distinct components: element identification, semantic bridge detection, graph embedding generation, and vector-based matching, allowing the system to handle complex relationships while maintaining modularity and manageable complexity
Solution Approach 2:
The patent combines multiple technical approaches (string-based similarity, graph theory, embedding techniques, and vector functions) into a composite mapping system that leverages the strengths of each method to handle diverse and complex taxonomy relationships across multiple documents
Data Source
AI summary
A control unit to map at least one element present in plurality of documents (i.e., a first document and a second document) is disclosed. The control unit identifies the at least one element in the plurality of documents and identifies at least one semantic bridge between the first document and the second document using string-based similarities. The control unit generates a corresponding taxonomy graph and a graph embedding for the at least one element of the first document and the second document. The control unit correlates the generated corresponding graph embeddings of the at least one element of the first document and the second document. The control unit maps the at least one element of the first document to the at least one element of the second document using a vector function.