Natural Language Query Disambiguation via Schema Join Paths
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Solution Overview
Problem
Natural language queries often exhibit linguistic ambiguities that complicate the interpretation of user intent, leading to inefficient query processing and the need for user intervention to resolve ambiguities.
Innovation Solution
The method involves converting natural language queries into structured operations by resolving linguistic ambiguities using join paths generated based on a knowledge base's data schema, allowing for automatic disambiguation and generation of unambiguous structured queries without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If natural language queries are converted directly to structured operations without resolving ambiguities, then query processing is simpler and faster, but the accuracy and correctness of results deteriorate due to multiple possible interpretations
Solution Approach 1:
The system performs preliminary disambiguation action by generating multiple possible interpretations of ambiguous terms and evaluating them against the data schema before final query execution. This preliminary analysis of join paths and column mappings resolves ambiguities in advance, allowing the system to select the most appropriate interpretation without requiring user intervention during query execution.
Solution Approach 2:
The system changes the parameter of query interpretation by generating multiple alternative meanings for ambiguous terms and systematically evaluating each against the knowledge base schema. By transforming a single ambiguous interpretation into multiple parameter variants and testing them against join paths and column mappings, the system identifies the correct interpretation that matches the intended query meaning.
2Measurement precision
If the system generates multiple interpretations to resolve ambiguities, then query accuracy improves, but computational effort and processing time increase
Solution Approach 1:
The system segments the disambiguation process into distinct phases: generating possible interpretations, mapping terms to data schema elements, evaluating join paths, and selecting the best match. By dividing the complex task of ambiguity resolution into smaller, manageable segments that can be processed independently and systematically, the system reduces overall computational complexity while maintaining accuracy.
Solution Approach 2:
The system applies partial action by generating only the necessary number of interpretations based on the data schema context rather than exhaustively exploring all possible meanings. By using the schema structure to prune unlikely interpretations and focus computational resources on plausible candidates, the system achieves accurate disambiguation with reduced computational effort.
3Measurement precision
If the system requires user intervention to resolve ambiguities, then query accuracy can be maintained, but ease of operation and user experience deteriorate
Solution Approach 1:
The system provides self-service by automatically resolving ambiguities through schema-based disambiguation without requiring user intervention. The system independently analyzes ambiguous terms, generates possible interpretations, evaluates them against the data schema and join paths, and selects the most appropriate meaning, thereby maintaining query accuracy while eliminating the need for user input or clarification requests.
Solution Approach 2:
The data schema acts as an intermediary that mediates between the ambiguous natural language query and the structured database operations. By introducing the schema as an intermediate layer that provides contextual constraints and guidance, the system automatically resolves ambiguities that would otherwise require user clarification, improving both ease of operation and maintaining accuracy.
Data Source
AI summary
One of the methods includes, obtaining a natural language query from a user; while converting the natural language query into structured operations to be performed on structured application programming interfaces (APIs) of a knowledge base: responsive to detecting a linguistic ambiguity associated with the converting, wherein the linguistic ambiguity identifies two or more interpretations of a word included in the natural langue query: resolving the linguistic ambiguity by selecting an interpretation of the word, from the two or more interpretations of the word, based on one or more join paths that have been generated based on a data schema of the knowledge base; modifying the converting in accordance with the selected interpretation of the word, to generate one or more structured operations; performing the one or more structured operations on the structured APIs of the knowledge base; and returning search results matching the natural language query results to the user.