Dynamic Query Example Generation via Token Fragmentation
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Solution Overview
Problem
Search engines and query-based systems face challenges in handling queries with imprecise syntax, where slight variations in punctuation or wording can lead to ambiguous interpretations, making it difficult to generate accurate answers or provide relevant results.
Innovation Solution
A method and system for dynamically generating example queries by partitioning user input into tokens, reassembling them into semantically meaningful fragments, and matching these fragments with query templates to produce alternative queries that can be processed for satisfactory answers, including scoring and categorizing these examples for user selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses strict syntax parsing to ensure accurate query interpretation, then query processing precision is improved, but user accessibility deteriorates because users cannot use natural or varied wording
Solution Approach 1:
The query is divided into semantic fragments or concepts, allowing flexible recombination. The system identifies key terms and their relationships rather than requiring fixed syntactic structures, enabling users to express queries in various natural ways while maintaining accurate interpretation.
Solution Approach 2:
The query processing system dynamically adapts to different syntactic variations and rephrasings. Instead of rigid parsing rules, the system uses flexible matching that can interpret equivalent meanings despite differences in wording, punctuation, or structure, thereby accommodating user variability while ensuring accurate processing.
2Adaptability or versatility
If the system generates multiple possible query interpretations to handle ambiguity, then query handling versatility is improved, but system complexity increases due to multiple parsing and matching pathways
Solution Approach 1:
By segmenting the query into discrete semantic fragments, the system can generate multiple interpretations systematically rather than through complex nested parsing rules. Each fragment can be combined in different ways to produce alternative query meanings, making the versatility manageable and structured.
Solution Approach 2:
The system uses template-based query generation where predefined query patterns are filled with extracted terms. This copying approach allows multiple valid query variations to be generated efficiently from templates rather than requiring complex creative generation algorithms, reducing system complexity while maintaining versatility.
3Measurement precision
If the system provides spellchecking and query correction features, then answer accuracy is improved, but user interaction time increases due to additional prompts and corrections
Solution Approach 1:
The system performs preliminary parsing and semantic analysis on the query before presenting results. By pre-processing the query to identify key concepts and potential ambiguities, the system can prepare multiple possible interpretations in advance, reducing the need for iterative user corrections and accelerating the overall interaction.
Solution Approach 2:
The system provides feedback by presenting multiple possible query interpretations along with their associated results. This allows users to quickly review and select the intended meaning without requiring explicit correction prompts, reducing interaction time while maintaining high query accuracy through intelligent suggestion.
Data Source
AI summary
In a method for generating example queries, an actual query is received, and the actual query is portioned into one or more tokens. One or more fragments of the actual query are generated based on reassembly of the one or more tokens. A plurality of example queries are generated based on different interpretations of one or more fragments, and at least one example query is outputted.