Natural Language Query Translation for Document Retrieval Precision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural language search queries in document repositories are often imprecise due to ambiguous words or phrases, leading to ineffective retrieval of relevant documents.
Innovation Solution
A method that detects search queries and generates modified queries by adding atomic tags based on static analysis and semantic rules, enriches tags through combinations, and reconciles conditions to create focused and accurate query language statements for document retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language search queries are used directly, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces an intermediary processing system that translates natural language queries into formal query language statements. The system uses semantic analysis, entity recognition, and query template matching as intermediate steps between user input and document retrieval, thereby maintaining ease of operation while improving measurement precision through structured query formulation.
Solution Approach 2:
The patent segments the natural language query into distinct components such as entities, attributes, constraints, and search terms. By breaking down the query into structured elements that can be individually processed and mapped to query language statements, the system improves precision while keeping the input interface simple and natural.
2Ease of operation
If natural language queries are used, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system employs intermediary processing layers including semantic analysis, entity resolution, and query validation that act as mediators between natural language input and document retrieval. These intermediaries ensure reliable interpretation of user intent and accurate mapping to query language statements, thereby improving retrieval accuracy without affecting ease of operation.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes query results and user interactions to refine query interpretation and retrieval accuracy. Through iterative refinement and validation of query language statements against document repository schemas, the system improves reliability while maintaining natural language input simplicity.
3Manufacturing precision
If query language statements are generated through static analysis and semantic rules, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary static analysis of the document repository schema and pre-defines query templates, entity lists, and semantic rules before query processing. By preparing these structural frameworks in advance, the system achieves high query generation precision through systematic rule application while managing complexity through organized preprocessing rather than complex real-time processing.
Solution Approach 2:
The system changes parameters from natural language variability to structured query language parameters through systematic transformation rules. By mapping natural language entities and attributes to standardized query language parameters using pre-defined semantic rules and templates, the patent achieves manufacturing precision in query generation while controlling device complexity through parameter standardization.
Data Source
AI summary
Techniques for generating query language statements for a document repository are described herein. An example method includes detecting a search query corresponding to a document repository and generating a modified search query by adding atomic tags to the search query, the atomic tags being based on prior knowledge obtained by static analysis of the document repository and semantic rules. The method also includes generating enriched tags based on combinations of the atomic tags and any previously identified enriched tags and generating a first set of conditions based on combinations of the atomic tags and the generated enriched tags and generating a second set of conditions based on free-text conditions. The method also includes generating the query language statements based on the first set of conditions and the second set of conditions and displaying a plurality of documents from the document repository that satisfy the query language statements.