Natural Language Query Translation via Ontology Feedback
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Solution Overview
Problem
Existing language translation techniques face difficulties in determining the correctness of interpretations and expressing complex semantics in a readable format, particularly when dealing with natural language queries and structured languages.
Innovation Solution
The method involves determining similarities and differences among multiple natural language query interpretations derived from an input query, generating natural language descriptions based on these analyses, and producing unambiguous natural language strings to represent the interpretations, which are then output to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple interpretations are generated from a natural language query, then the system can capture different semantic meanings, but it becomes difficult to determine which interpretation is correct
Solution Approach 1:
The system implements feedback by translating interpretations back to natural language and comparing them with the original query. This feedback loop allows the system to evaluate which interpretations align best with the user's intended meaning, thereby determining correctness among multiple interpretations.
Solution Approach 2:
The patent introduces natural language translation as an intermediary mechanism between the structured query interpretations and the evaluation process. By converting interpretations to natural language and comparing with the original query, the system creates a mediating layer that helps assess interpretation correctness without directly analyzing structured semantics.
2Measurement precision
If complex query interpretations are expressed in detailed format, then semantic accuracy is improved, but readability for users deteriorates
Solution Approach 1:
Instead of directly presenting complex structured interpretations to users, the system inverts the approach by translating interpretations back into natural language. This inversion converts difficult-to-understand structured formats into familiar, readable natural language while preserving semantic meaning through the translation process.
Solution Approach 2:
Natural language translation serves as an intermediary that bridges the gap between semantically accurate but complex structured interpretations and user-friendly readable format. The translation process maintains semantic fidelity while presenting information in an accessible manner.
Data Source
AI summary
Methods, systems, and computer program products for translating structured languages to natural language using domain-specific ontology are provided herein. A computer-implemented method includes determining similarities among multiple natural language query interpretations derived from an input query, determining differences among the multiple natural language query interpretations, and generating natural language descriptions of each of the multiple natural language query interpretations based on analysis of the determined similarities, the determined differences, and the input query. The method also includes producing, for each of the natural language query interpretations, a natural language string that represents one or more unambiguous interpretations of the input query, wherein the producing comprises consolidating the generated natural language descriptions. Further, the method includes outputting each of the produced natural language strings to a user.