Recycling Match Weights for Database Entity Linking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database systems face challenges in efficiently identifying and linking entity representations across incomplete or restricted databases, particularly in handling internally inconsistent and fuzzy search criteria, while ensuring compliance with access restrictions and maintaining data security.
Innovation Solution
The technique employs field match templates to partition search criteria into fixed, optional, and extra credit fields, and utilizes statistical measures to calibrate search results, allowing for efficient entity representation identification and matching across universal and foreign databases, even when criteria and databases are incomplete, through methods like batch processing and fuzzy matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional database systems are used to identify and link entity representations across incomplete databases, then data security and access restriction compliance are maintained, but processing speed decreases and computational resources are consumed
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing match weights for different field combinations before actual entity linking is needed. When queries are executed, these pre-computed weights are retrieved and applied, avoiding redundant calculations and significantly improving processing speed while maintaining security compliance through controlled access to the pre-computed data structures.
Solution Approach 2:
The patent segments the entity linking process into distinct phases: computing match weights for individual fields, combining these weights according to different field combinations, and finally ranking entity representations. This segmentation allows each phase to be optimized independently and enables parallel processing of multiple field combinations, improving overall productivity without compromising security protocols.
2Measurement precision
If comprehensive search criteria are used to accurately identify entity representations, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system manages complexity by dynamically changing parameters based on query requirements. Different field combinations are assigned different weight multipliers based on their discriminative power and the specific query context. This parameter adjustment allows the system to achieve high measurement precision for complex queries while maintaining manageable system complexity through adaptive parameter tuning rather than fixed complex structures.
Solution Approach 2:
The patent introduces match weights as intermediary values that mediate between raw field comparisons and final entity identification. These weights serve as intermediate calculations that simplify the complexity of evaluating multiple field combinations by reducing them to comparable numerical values, making the overall system more manageable while maintaining high identification accuracy.
3Adaptability or versatility
If multiple field combinations are evaluated to handle fuzzy search criteria, then adaptability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary computations to establish match weights for various field combinations before actual entity linking operations. This pre-computation stores the results of complex weight calculations in accessible data structures, allowing the system to handle fuzzy search criteria with multiple field combinations rapidly during query execution, thereby improving adaptability while minimizing time loss.
Solution Approach 2:
The patent implements partial action by evaluating field combinations in order of their expected discriminative power. The system computes match weights for the most important field combinations first and can terminate the evaluation process once sufficient confidence is achieved, rather than exhaustively evaluating all possible combinations. This approach maintains high adaptability for fuzzy matching while significantly reducing computation time by avoiding unnecessary calculations.
Data Source
AI summary
Disclosed is a system for, and method of, recycling field value weights as computed for database linking purposes. Such field value weights may be used for a search operation. In some embodiments, such weights may be used for a search operation prior to their values stabilizing during an iterative linking operation.