Automated Abbreviation Mapping via Similarity and Collocation
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Solution Overview
Problem
The challenge lies in associating meaningful expressions with abbreviated names in data stores, as existing methods require significant human effort and face difficulties due to poor documentation and security limitations when accessing sensitive content.
Innovation Solution
A system and method that select meaningful terms based on similarity and collocation criteria, using regular expressions to expand abbreviated terms and associate them with meaningful expressions, while processing search queries and user feedback to improve relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter experts manually map abbreviated names to meaningful expressions, then mapping accuracy is improved, but human effort and time requirements increase significantly
Solution Approach 1:
The system performs self-service by automatically generating mappings between abbreviated names and meaningful expressions using machine learning models, eliminating the need for manual expert intervention while maintaining high mapping accuracy through automated pattern recognition and natural language processing
Solution Approach 2:
The patent replaces the mechanical manual mapping process with an automated computational system that uses machine learning algorithms to generate mappings, substituting human cognitive effort with algorithmic processing that scales efficiently to large datasets
2Speed
If search engines process abbreviated names directly, then search speed is maintained, but search accuracy deteriorates due to inability to understand abbreviations
Solution Approach 1:
The system performs preliminary expansion of abbreviated names to their full meaningful expressions before search processing, allowing search engines to work with comprehensible terms while maintaining efficiency through pre-computed mapping relationships that enable rapid lookup and substitution
3Measurement precision
If data stores are accessed to analyze actual data for mapping, then mapping relevance is improved, but security limitations prevent access to sensitive content
Solution Approach 1:
The patent introduces an intermediary layer that processes abbreviated names and generates mappings without requiring direct access to sensitive data contents, using metadata and structural patterns as intermediaries to create accurate mappings while maintaining security boundaries
Solution Approach 2:
The system creates copies of non-sensitive metadata and structural information from data stores to generate mappings, avoiding the need to access or expose sensitive actual data contents while still deriving meaningful mapping relationships from available information
Data Source
AI summary
A method of associating a meaningful term with a first abbreviated name includes selecting a first meaningful term based on similarity between the first meaningful term and expansion of a first abbreviated term, selecting the first meaningful term based on collocation of a second abbreviated term, and associating the first meaningful term with the first abbreviated term. The first abbreviated term is associated with a first abbreviated name. The second abbreviated term and a third abbreviated term are associated with a second abbreviated name. The second abbreviated term satisfies a matching criterion associated with the first abbreviated term. A corresponding system and computer-readable device are also disclosed.