Semantic Matching Vectors for Culturally Aware Real-Time Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing semantic matching technologies fail to accurately compare heterogeneous occupational data records due to ignoring cultural and linguistic differences, and they are inefficient in processing large datasets, leading to inaccurate results 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 cultural and linguistic nuances.
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 of matching results deteriorates due to ignoring cultural and linguistic 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 matching process with cultural and linguistic context, thereby improving accuracy without significantly complicating the overall system implementation.
Solution Approach 2:
The patent adds new dimensions to the matching process by incorporating cultural context, linguistic nuances, and semantic relationships beyond simple keyword or string comparison. This multi-dimensional approach improves matching accuracy while maintaining reasonable implementation complexity through modular architecture.
2Measurement precision
If vector operations are performed in high dimensional vector space for semantic matching, then the matching capability is enhanced, but the computational performance deteriorates
Solution Approach 1:
The patent segments the high-dimensional vector space into multiple lower-dimensional subspaces or clusters. By dividing the computational task into smaller segments, the system maintains the benefits of high-dimensional semantic representation while reducing the computational burden of operations in the full high-dimensional space.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing semantic relationships, cultural context data, and vector representations in databases before actual matching operations. This preprocessing reduces the computational load during runtime, enabling faster matching performance while maintaining high-dimensional semantic accuracy.
3Measurement precision
If manual classification or individually self-prioritization is performed for semantic matching, then the matching accuracy is improved, but the processing time and effort deteriorate
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform semantic analysis, cultural context integration, and matching operations without requiring manual classification or individual prioritization. The automated semantic matching engine processes data independently, achieving high accuracy while eliminating manual time investment.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system learns from matching results and continuously improves its semantic understanding and cultural context application. This automated feedback loop enables the system to achieve and maintain high matching accuracy without increasing manual processing time.
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.