Query Refinement for Hierarchical Search Disambiguation
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Solution Overview
Problem
Existing question-answer systems often produce unwieldy and irrelevant search results due to query specificity, ambiguity, or domain issues, leading to inefficient information retrieval and resource wastage.
Innovation Solution
Implementing techniques for query refinement, such as backoff and drilldown searches, query rewriting, hierarchical data collection, and relevance detection to enhance search accuracy and efficiency, including machine learning models for disambiguation and preference-based data collection prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the query is made more specific to improve search precision, then the relevance of search results improves, but the risk of ambiguity and domain issues increases leading to unwieldy and irrelevant results
Solution Approach 1:
The system segments the query processing into multiple stages: initial query analysis, backoff search for broader concepts, drilldown search for specific concepts, and iterative refinement. This segmentation allows the system to handle query specificity and ambiguity systematically through multiple passes rather than a single complex processing step.
Solution Approach 2:
The system performs preliminary actions by conducting backoff searches before drilldown searches, and performing relevance detection before final result selection. This preliminary processing identifies potential ambiguity and domain issues early, allowing the system to refine queries iteratively before committing to final search results.
2Measurement precision
If backoff and drilldown searches are implemented to handle query specificity, then search accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system uses periodic action by implementing iterative refinement where backoff and drilldown searches are performed in alternating cycles. The system performs a backoff search, evaluates results, then performs a drilldown search, evaluates again, and repeats until convergence or maximum iterations are reached. This periodic approach balances thoroughness with efficiency.
Solution Approach 2:
The system applies partial action by performing backoff and drilldown searches selectively based on query characteristics. Relevance detection determines whether a query is ambiguous or out-of-domain, and only then are backoff/drilldown searches triggered. This avoids unnecessary processing for clear, in-domain queries while providing enhanced processing when needed.
3Measurement precision
If query rewriting is performed to correct query flaws, then search result quality improves, but the complexity of the searching system increases
Solution Approach 1:
The system introduces an intermediary component - the query rewriting module with relevance detection - that sits between query submission and search execution. This intermediary analyzes queries for ambiguity and domain issues, then rewrites them appropriately before searching. This modular intermediary approach improves result quality while containing complexity in a dedicated component rather than distributing it throughout the entire system.
4Productivity
If hierarchical data collection with user preferences is implemented, then information retrieval efficiency improves, but system complexity and data management requirements increase
Solution Approach 1:
The system adds another dimension to data management by organizing data collections hierarchically with user preferences layered on top. Instead of flat data storage, the system creates a multi-dimensional structure where data collections are organized by hierarchy levels, and user preferences add an additional filtering dimension. This hierarchical organization improves retrieval efficiency by enabling targeted searches while managing complexity through structured data architecture.
Data Source
AI summary
Disclosed are methods, systems, devices, apparatus, media, and other implementations for improved search-time content retrieval performed by an information retrieval platform. The implementations include a method including determining by a searching system, at a first time instance, one or more first search results for a first query comprising one or more query terms associated with a first concept, modifying the query, based on the determined one or more first search results, to include one or more modified query terms associated with one or more concepts hierarchically related to the first concept, and determining at a subsequent time instance one or more subsequent search results for the modified query comprising the one or more modified query terms.