Fuzzy Rule Database Search System with Adaptive Learning
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Solution Overview
Problem
Current database search technologies face challenges in accurately retrieving documents due to incorrect spellings, word order variations, and cultural differences in names and addresses, leading to inefficient and inaccurate results, especially in industry-specific searches where intuitive knowledge plays a crucial role.
Innovation Solution
A system that uses adaptable fuzzy rules for database searches, where user interactions and selections are used to modify and refine the search algorithms, allowing the system to learn and improve over time by filtering and purging less accurate data samples, thereby enhancing search accuracy tailored to specific industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword search is used, then search speed is fast, but search accuracy deteriorates due to misspellings and word order variations
Solution Approach 1:
The system pre-generates multiple spelling variations and word order permutations of search keywords before executing the search. This preliminary expansion of search terms ensures that misspelled or reordered keywords still match relevant documents, improving search accuracy without requiring multiple sequential search attempts that would increase time consumption.
Solution Approach 2:
The patent introduces an intermediary processing layer between the user's search query and the database search execution. This intermediary component analyzes the input keywords, generates variations, and manages multiple search operations, thereby shielding users from the time cost while maintaining high accuracy through comprehensive matching.
2Measurement precision
If fuzzy search algorithms with phonetic matching are used, then search accuracy improves, but device complexity increases
Solution Approach 1:
The patent divides the fuzzy search functionality into separate, independent modules: one module handles phonetic matching while another handles word order variations, and a third manages spelling corrections. This segmentation allows each module to specialize in a specific type of fuzzy matching without requiring the entire system to be overly complex, making the overall system more manageable and maintainable.
Solution Approach 2:
The search system is designed to handle multiple types of fuzzy matching (phonetic, word order, spelling) through a unified framework. This multi-functional approach allows the same core search infrastructure to serve multiple purposes, reducing overall system complexity compared to having separate specialized systems for each type of fuzzy matching.
3Measurement precision
If industry-specific intuitive rules are captured, then search accuracy improves, but device complexity increases
Solution Approach 1:
The system automatically captures and learns industry-specific search patterns and intuitive rules from user interactions and search results, rather than requiring manual programming of these rules. This self-learning capability allows the system to adapt to specific industry needs over time without increasing the complexity of the underlying rule system, as the complexity is dynamically generated rather than statically defined.
Solution Approach 2:
The patent implements a mechanism where search parameters and rules can be dynamically adjusted based on industry-specific requirements. Rather than hardcoding complex industry rules, the system allows parameters to be modified based on learned patterns, enabling high accuracy while keeping the base system relatively simple and adaptable.
4Measurement precision
If extensive training data is stored, then learning accuracy improves, but loss of information increases due to storage constraints
Solution Approach 1:
The system extracts and stores only the most informative and representative training samples from the vast amount of available data. By selectively removing redundant or less valuable samples, the system maintains high learning accuracy while minimizing storage requirements, effectively managing the trade-off between data volume and information quality.
Solution Approach 2:
The patent applies different quality standards to different portions of the training data based on their informational value. High-value, informative samples are retained with full detail, while redundant samples are removed or summarized. This local quality approach ensures that the limited storage space is dedicated to the most valuable information, maintaining learning accuracy without requiring extensive storage.
Data Source
AI summary
An apparatus and method for database searching using fuzzy rules is presented. The apparatus and method may accept a word or word phrase such as a persons name and returns fuzzy rules for database searching. Applicable search rules are selected and word or word phrase equivalents are displayed to a user. The user accepts or rejects each of the word or word phrase. The word or word phrase along with the user's acceptance or rejection are stored in a sample database. The fuzzy rules are modified according to the data in the sample database. The database is filtered by training and testing portions of the database for accuracy and purging the least accurate portions.