Adaptive Query Expansion for Cross-Lingual Document Retrieval
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Solution Overview
Problem
Current search systems face challenges in efficiently retrieving relevant documents across vast, linguistically diverse datasets, particularly in patent and academic research, due to limitations in handling linguistic variations, terminological differences, and the need for precise relevance ranking, which leads to suboptimal recall and precision.
Innovation Solution
The proposed solution enhances search systems by implementing focus-spectrum expansion, cross-lingual search term suggestions, and semantic search expansion using analytics to improve relevance ranking, allowing for better handling of linguistic variations and increased recall without compromising precision, through automated query modification and the use of structured data fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Boolean OR clauses are added to amplify search queries for better recall, then more relevant documents are retrieved, but precision deteriorates with unrelated results
Solution Approach 1:
The system dynamically adjusts search parameters and query structure based on initial results analysis. It transforms static Boolean queries into adaptive multi-phase searches that modify clauses based on retrieved document characteristics, allowing the system to optimize between recall and precision automatically
Solution Approach 2:
The invention changes search parameters by analyzing initial results and generating modified queries with adjusted term weights, clause structures, and selection criteria. It uses statistical analysis of retrieved documents to dynamically modify search parameters for subsequent phases, improving both recall and precision
2Measurement precision
If Boolean AND clauses are added to amplify search queries for better precision, then fewer false hits are retrieved, but recall deteriorates with missed relevant documents
Solution Approach 1:
The system dynamically adjusts search parameters and query structure based on initial results analysis. It transforms static Boolean queries into adaptive multi-phase searches that modify clauses based on retrieved document characteristics, allowing the system to optimize between recall and precision automatically
Solution Approach 2:
The invention performs preliminary analysis of initial search results to identify relevant document characteristics, terminology patterns, and field distributions before generating modified queries. This preliminary action informs subsequent query modifications to improve recall without sacrificing precision
3Measurement precision
If manual iteration is performed to balance recall and precision, then search quality improves, but time consumption increases
Solution Approach 1:
The system performs self-service by automatically analyzing initial search results, identifying relevant patterns, and generating optimized queries without requiring manual intervention. The automated multi-phase search process iteratively improves results while minimizing time consumption through algorithmic optimization
Solution Approach 2:
The invention implements feedback mechanisms where initial search results are analyzed to generate statistical information that feeds into subsequent query modifications. The system uses feedback from retrieved documents to automatically adjust search parameters and terminology for improved results
4Reliability
If search queries are expanded with multiple terminology variants for different languages and grammatical forms, then recall improves, but query complexity increases
Solution Approach 1:
The system dynamically generates and manages terminology variants based on initial results analysis rather than using static, pre-defined query expansions. It adapts the complexity of query expansions based on the specific characteristics of retrieved documents, reducing unnecessary complexity while maintaining recall
Solution Approach 2:
The invention performs preliminary analysis of initial search results to identify the specific terminology variants and linguistic forms actually present in relevant documents. This preliminary action allows the system to expand queries with only the necessary variants rather than all possible forms, reducing query complexity
Data Source
AI summary
The present invention provides a method and system for identifying, retrieving and presenting electronic documents responsive to user queries. The three distinct inventive concepts are relevancy ranking of responsive documents based on component query technique; cross-lingual searching; and search expansion using analytics of initial results to derive and generate a modified query. Each of these inventions enhances document search and retrieval systems and the three solutions may be used separately or in any combination. The three inventions apply in layers above an underlying search system, controlling the submission of requests to the underlying system in support of received search requests, typically originating with an end user. Invention (III) provides a semantic-expansion capability specifically utilizing the availability of language independent fields in the data being searched with natural language query terms. This invention is enhanced by incorporating the preceding two inventions.