Query Classification System for Search Routing Efficiency
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Solution Overview
Problem
Current search systems face inefficiencies in routing query phrases to relevant backend databases, leading to a low ratio of relevant search results, as they often send queries to all databases, resulting in costly and inefficient processing.
Innovation Solution
Implementing a selectional preference query classification technique that classifies query phrases into categories by parsing them into constituent parts, matching these parts with stored patterns, and routing them to appropriate subsets of backend databases based on these classifications, while also using a combination of manual and supervised machine learning techniques for accurate categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query phrases are sent to all backend databases, then comprehensive search coverage is achieved, but processing costs increase and search efficiency decreases
Solution Approach 1:
The patent segments the backend databases into different categories (e.g., image databases, video databases, text databases) and classifies query phrases into corresponding categories. This segmentation allows the system to route queries to only the relevant database categories rather than all databases, thereby maintaining comprehensive search coverage for the appropriate domain while significantly improving search efficiency by avoiding unnecessary processing in irrelevant databases.
2Productivity
If query phrases are routed to selected backend databases based on classification, then processing costs are reduced and search efficiency is improved, but the ratio of relevant search results decreases
Solution Approach 1:
The patent implements feedback mechanisms where classification accuracy is continuously monitored and improved. The system uses training data to refine classification models, ensuring that queries are accurately routed to the correct database categories. This feedback loop maintains a high ratio of relevant search results while preserving the efficiency benefits of selective routing, as the classification system becomes increasingly accurate over time.
3Measurement precision
If manual classification techniques are used for query phrases, then classification accuracy is maintained, but processing time and operational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-classifying training query phrases into categories before the actual search operation. Classification patterns and rules are established in advance through training processes, allowing the system to quickly classify incoming queries without requiring real-time manual intervention. This preliminary preparation maintains high classification accuracy while dramatically reducing processing time during actual search operations.
4Productivity
If automated classification systems are implemented, then processing speed is improved, but classification accuracy and reliability decrease
Solution Approach 1:
The patent introduces an intermediary classification layer that bridges automated processing and accurate categorization. This intermediary system uses trained classification models and patterns to translate automated query analysis into accurate category assignments. The intermediary classification mechanism maintains high processing speed while ensuring classification accuracy by leveraging pre-learned patterns and relationships between queries and database categories.
Data Source
AI summary
A query phrase may be automatically classified to one or more topics of interest (e.g., categories) to assist in routing the query phrase to one or more appropriate backend databases. A selectional preference query classification technique may be used to classify the query phrase based on a comparison between the query phrase and patterns of query phrases. Additionally, or alternatively, a combination of query classification techniques may be used to classify the query phrase. Topical classification of a query phrase also may be used to assist a search system in delivering auxiliary information to a user who entered the query phrase. Advertisements, for instance, may be tailored based on classification rather than query keywords.