Title Normalization via N-gram Label Mapping
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Solution Overview
Problem
The lack of standardization in user-entered job titles and other data on social networks hinders effective searching, analysis, and aggregation, as variations in formatting, spelling, and abbreviations make it difficult to recognize semantically equivalent data.
Innovation Solution
A method and system for normalizing raw titles to canonical titles by generating n-grams, assigning attributes and labels, and using these to map new titles to existing canonical titles, with validation through white-box and black-box methods, enabling efficient search, analysis, and aggregation of semantically equivalent data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual review and mapping of user-entered data is performed, then data standardization is achieved, but time consumption and labor resources increase significantly
Solution Approach 1:
The system enables automatic self-service normalization by using machine learning models and n-gram analysis to autonomously identify and map data variants to canonical forms without requiring manual review for each entry, thus achieving data standardization while minimizing time consumption
Solution Approach 2:
The system performs preliminary action by pre-processing user-entered data through normalization algorithms and storing mapped relationships in advance, so that subsequent searches and analyses can directly utilize standardized data without requiring real-time manual intervention
2Reliability
If manual mapping of data variants is performed, then equivalent data terms are identified, but the scope is limited to manually mapped variants only
Solution Approach 1:
The system introduces n-grams as intermediary elements that bridge user-entered data variants and canonical forms. By analyzing overlapping n-grams between variants and canonical titles, the system can automatically identify equivalence relationships for data variants that were not manually mapped, thus expanding coverage while maintaining reliability
Solution Approach 2:
The system replaces the mechanical manual mapping process with automated computational algorithms including n-gram analysis, similarity scoring, and machine learning models. This substitution enables the system to handle unlimited data variants beyond what can be manually mapped, significantly expanding adaptability while maintaining or improving identification reliability
3Ease of operation
If data is entered in any format by users, then ease of data entry is improved, but data searching and aggregation become difficult
Solution Approach 1:
The system segments user-entered titles into n-grams (subsequences of words) and compares these segments against canonical titles. This segmentation allows the system to match titles even when formatted differently, preserving ease of entry while enabling effective search through partial or flexible matching of title components
Data Source
AI summary
A system for determining a canonical title includes an interface and a processor. The interface is to receive a new title. The processor is to generate a set of n-grams based on the new title; look up n-grams in canonical database to assign one or more labels; and map the new title to a canonical title based on the one or more labels.