Natural Language Request Generation for Data Processing Systems
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Solution Overview
Problem
Users face difficulties in formulating precise requests to data processing systems due to lack of knowledge about the underlying data model, leading to ambiguity in content retrieval, especially with complex queries and language constraints in existing semantic search engines.
Innovation Solution
A method for semiautomatic generation of requests in natural language, where user inputs are analyzed and converted into query language by resolving lexemes, which are processed sequentially to determine their meaning within the data model, allowing for intuitive input assistance and conversion into query language parameters, including data sources, classes, and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users formulate requests using natural language without knowledge of the data model, then ease of operation is improved, but measurement precision of the request deteriorates
Solution Approach 1:
The patent introduces a semantic analysis system as an intermediary between the user's natural language input and the structured data model. This mediator automatically interprets the natural language request, maps it to the appropriate data classes and attributes, and generates the corresponding query language request, thereby resolving the contradiction between ease of use and precision.
Solution Approach 2:
The system performs preliminary analysis and interpretation of the natural language input before executing the data retrieval. By pre-processing the user's request to identify intended data classes, attributes, and relationships, the system ensures precise query generation without requiring the user to have prior knowledge of the data model structure.
2Ease of operation
If semantic search engines are used to interpret natural language, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex semantic search engine into distinct functional modules: a natural language analysis component that processes user input, a mapping component that connects natural language terms to data model elements, and a query generation component that constructs the final request. This segmentation reduces overall system complexity by making each component's function specialized and manageable.
3Measurement precision
If complex queries are processed with full semantic analysis, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system applies partial semantic analysis by focusing on the most critical aspects of the natural language input needed to construct an accurate query. Rather than performing exhaustive analysis of all possible meanings and contexts, the system identifies and processes the essential elements required for precise data retrieval, thereby reducing processing time while maintaining accuracy.
Data Source
AI summary
The present invention relates to a method for an at least semiautomatic generation of a request to a data processing system with a data pool, wherein a data model, which is usable for the selection of data from the data pool using a query language, forms the basis of the data pool, wherein a user's request made in natural language, which takes place by a user input of terms (user terms) captured as a series of characters (character string), wherein the terms semantically describe the user intended results of the answer of the data processing system on the request that has to be carried out by the data processing system, is analyzed and/or interpreted, and converted into a request in the query language.