Natural Language Query Entity Tagging for Database Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As databases grow in size due to increasing data storage needs, query searching becomes slower, making it inefficient to retrieve specific data within retail databases.
Innovation Solution
A method using natural language processing to apply condition-action rules to user-entered search queries, identifying entity identifiers and creating a search string that enables quick access to relevant data by associating phrases with entity identifiers and taxonomy variants, thereby optimizing database searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If database size increases to store more data, then data storage capacity is improved, but query search time increases
Solution Approach 1:
The patent segments the query processing into distinct phases: natural language processing to identify entity tags, schema mapping to translate tags to database fields, and conditional filtering to apply business rules. This segmentation allows each phase to optimize for its specific function, improving overall query efficiency in large databases.
Solution Approach 2:
The system performs preliminary actions by pre-defining the internal schema with entity tags and their mappings to database fields before queries are executed. This advance preparation allows the query processor to quickly match natural language entities to database structures without performing complex analysis during query execution, thereby reducing search time in large databases.
2Measurement precision
If natural language processing is applied to identify entity tags, then query accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent introduces an internal schema as an intermediary layer between natural language queries and the database structure. This schema contains predefined entity tags and their mappings to database fields, acting as a mediator that translates user-friendly natural language into precise database queries, thereby improving accuracy while managing complexity through structured intermediation.
Solution Approach 2:
The system changes parameters by transforming natural language entity tags into standardized database field names and values according to the internal schema. This parameter transformation allows the system to maintain high query accuracy by ensuring precise mapping to database structures while managing complexity through systematic parameter conversion rules.
Data Source
AI summary
Embodiments of the present invention disclose a method, computer program product, and system for searching a database using a user entered search query. A search query for a database is received by the computer and the computer applies condition-action rules based on natural language processing rules to identify one or more phrases within the search query that is associated an entity identifier. The computer further identifies any taxonomy variants that have been established for the identified phrases. The computer creates a search string that includes search query and the entity identifiers. The database search is conducted by the computer and the results are displayed for the user.