Query Expansion Using Unstructured Data Frequency and Semantic Analysis
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Solution Overview
Problem
Existing query expansion techniques in information retrieval often face challenges in achieving high recall without compromising precision, particularly when queries are based on limited or narrowly defined criteria, leading to exclusion of relevant text content with alternative labels or expressions.
Innovation Solution
A method that selects additional query terms from unstructured data based on frequency of occurrence and semantic analysis, where terms are added to the query criteria if they have an inferred similarity to the original terms, while removing redundant matches to maintain precision, thereby enhancing recall without reducing the relevance of search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query expansion includes all related terms from a knowledge base, then recall is improved, but precision deteriorates
Solution Approach 1:
The patent changes the parameter of term selection by introducing frequency thresholds and relevance scoring. Instead of including all related terms, the system filters terms based on their frequency of occurrence in the knowledge base and their relevance score to the original query, thereby maintaining recall while improving precision through parameter-based filtering
Solution Approach 2:
The patent applies partial action by selectively expanding the query with only the most relevant terms rather than all possible related terms. The system performs query expansion to a controlled extent using frequency thresholds and relevance scoring, avoiding the excessive inclusion of unrelated terms that would harm precision
2Measurement precision
If query criteria include limited information or are narrowly specified, then precision is maintained, but recall deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-processing the query to identify potential expansion terms from the knowledge base before executing the final search. The system pre-calculates frequency thresholds and relevance scores for candidate terms, preparing an expanded query set that maintains precision while improving recall
3Reliability
If query expansion adds many additional terms, then recall is improved, but the complexity of query processing increases
Solution Approach 1:
The patent manages query processing complexity by introducing parameter-based filtering mechanisms. The system uses frequency thresholds and relevance scoring parameters to automatically filter and prioritize expansion terms, reducing the manual complexity of query construction while maintaining high recall through systematic term selection
Data Source
AI summary
A processor determines a set of terms frequently occurring within unstructured data associated with criteria of a query. The processor analyzes the unstructured data to match a term of the set of terms, to criteria of the query. Matching terms are retained and unmatched terms are checked for semantic similarity to terms of the criteria to determine an inferred match of the term of the unstructured data to terms of the criteria of the query. In response to determining an inferred match, the inferred match term is added to the second set of terms, and the processor compares the second set of terms to the terms of the criteria of the query, and removes matching terms, resulting in a third set of terms added to the set of criteria terms of the query. The additional query terms will enhance the recall without diluting the precision.