Natural Language Query Processing via Statistical Semantic Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems require users to manually locate data or use technical query languages to retrieve information from databases, which can be cumbersome and error-prone, especially when dealing with natural language queries.
Innovation Solution
A query-processing system that uses natural language processing techniques to match semantic intents in user queries to relevant data elements within a data model, calculating term frequency and inverse document frequency scores to determine contextual significance, thereby constructing database queries to retrieve data without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually locate data or use technical query languages to retrieve information from databases, then data retrieval accuracy is improved, but user overhead and operation complexity increase
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between the user and the database system. The NLP module translates user-friendly natural language queries into structured database queries automatically, eliminating the need for users to learn technical query languages while maintaining accurate data retrieval through statistical matching and semantic analysis
Solution Approach 2:
The system performs automatic query translation and data element matching without requiring manual user intervention. The statistical processing automatically calculates term frequencies, inverse document frequencies, and contextual significance scores to identify relevant data elements, enabling the system to serve itself in translating and executing queries
2Measurement precision
If users manually locate data or use technical query languages, then query result precision is improved, but device complexity and learning requirements increase
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between the user and the database system. The NLP module translates user-friendly natural language queries into structured database queries automatically, eliminating the need for users to learn technical query languages while maintaining accurate data retrieval through statistical matching and semantic analysis
Solution Approach 2:
The system dynamically adjusts statistical parameters such as term frequency weights, inverse document frequency thresholds, and contextual significance scores to optimize query matching. By changing these parameters based on the specific query and data model, the system achieves high precision results without requiring complex manual configuration
3Measurement precision
If statistical processing with term frequency and inverse document frequency calculations is performed, then query matching accuracy is improved, but processing time increases
Solution Approach 1:
The patent pre-calculates and stores statistical properties of the data model including term frequencies, inverse document frequencies, and contextual significance scores for all data elements. This preliminary processing allows the query matching phase to quickly retrieve and compare pre-computed statistics rather than calculating them from scratch for each query, significantly reducing processing time while maintaining high matching accuracy
Data Source
AI summary
Techniques for processing a natural language query are provided. The techniques include obtaining a set of statistics representing a contextual significance of a set of words in a data model for a data set. Next, the set of statistics is used to match a set of semantic intents for a natural language query of the data set to one or more data elements in the data model. The data element(s) and the data set are then to generate a result of the natural language query, and the result is provided in a response to the natural language query.