Semantic Matching for Multilingual Occupational Data Vectors
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Solution Overview
Problem
Existing semantic matching technologies fail to accurately compare heterogeneous data records in culturally diverse and multilingual domains due to ignoring occupational criteria, cultural and language-specific differences, and inefficiencies in high-dimensional vector operations, leading to inaccurate matches and high computational costs.
Innovation Solution
A system that represents occupational data records as vectors in a high-dimensional non-orthogonal unit vector space, applies correlation coefficients from an ontology, and performs parallel processing to determine similarity, considering occupation-specific criteria and cultural contexts, using asymmetric comparisons and cosine similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based approaches or NLP similarity techniques are used for semantic matching, then the matching process is simple to implement, but the accuracy is low due to ignoring semantic context, cultural peculiarities, and multilingual differences
Solution Approach 1:
The patent introduces an intermediary layer (semantic analysis module with ontology and cultural context database) between the input data and the matching algorithm. This intermediary enriches the raw data with semantic meanings, cultural contexts, and linguistic nuances before comparison, thereby improving matching accuracy without significantly complicating the overall system architecture
Solution Approach 2:
The patent replaces simple keyword-based mechanical string matching with a more sophisticated semantic analysis system that uses natural language processing, ontology-based reasoning, and cultural context integration. This substitution transitions from a purely syntactic approach to a semantic-aware approach, significantly improving matching precision
2Measurement precision
If manual classification or individually self-prioritization is used to find matches, then the matching accuracy can be high, but the process is tedious and impractical for large data sets
Solution Approach 1:
The patent segments the matching process into distinct modular components: data preprocessing module, semantic analysis module, ontology-based reasoning module, and matching algorithm module. Each module handles a specific aspect of the matching task, allowing the system to maintain high accuracy through careful semantic analysis while achieving automation and scalability for large datasets
Solution Approach 2:
The patent implements an automated system that performs semantic analysis, cultural context integration, and matching operations without requiring manual intervention. The system self-adjusts by learning from data patterns and automatically prioritizes matches based on semantic relevance, eliminating the need for tedious manual classification while maintaining high accuracy
3Measurement precision
If vector operations are performed in high dimensional vector space to improve semantic matching, then the matching capability is enhanced, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the high-dimensional vector space into multiple lower-dimensional subspaces based on semantic categories, cultural contexts, or linguistic features. By performing vector operations in these smaller subspaces rather than the full high-dimensional space, the system maintains semantic matching capability while significantly reducing computational complexity and processing time
Solution Approach 2:
The patent applies partial vector operations by focusing computations only on relevant dimensions or subsets of the vector space that are most important for the specific matching task. This selective approach performs sufficient semantic analysis without the excessive computational cost of processing all dimensions, achieving a balance between accuracy and efficiency
Data Source
AI summary
A computer-based system and method for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance. A plurality of occupational data records is generated and, for each of the occupational data records, a respective vector is created to represent the occupational data record. Each of the vectors is sliced into a plurality of chunks. Thereafter, semantic matching of the chunks occurs in parallel, to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time. Thereafter, values representing similarities between at least two of the occupational data records are output.