Search Query Semantic Interpretation Evaluation
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Solution Overview
Problem
Search engines face challenges in determining user intent for ambiguous search queries, as they may refer to multiple entities or concepts, leading to suboptimal search results due to ambiguity.
Innovation Solution
The system evaluates different semantic interpretations of a search query by generating modified queries based on canonical queries, comparing search results, and determining the degree of similarity to select the most accurate interpretation, thereby predicting user intent and improving search result quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search engine uses a single interpretation of an ambiguous search query, then the search response time is fast, but the search result quality deteriorates due to inability to capture user intent accurately
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple semantic interpretations and their associated canonical queries before receiving the actual search query. When a query arrives, the system can quickly match it against pre-computed interpretations rather than generating them in real-time, thus improving response time while maintaining multiple interpretation coverage
Solution Approach 2:
The system segments the search query analysis process into distinct semantic interpretations, each with its own canonical query. This allows the system to process and evaluate multiple interpretations independently and in parallel, improving overall efficiency while maintaining comprehensive coverage of possible user intents
2Measurement precision
If the search engine generates multiple modified search queries to evaluate different semantic interpretations, then the semantic interpretation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system creates modified search queries by copying and adapting the original query structure. Each modified query is generated by replacing terms in the original query with terms from the associated canonical query, preserving the original query's structure while incorporating semantic interpretation-specific terminology. This copying approach simplifies the generation process compared to creating entirely new queries from scratch
Solution Approach 2:
The system changes parameters such as search terms and query structure based on the selected semantic interpretation. By modifying only the necessary parameters (replacing specific terms with their canonical equivalents) rather than redesigning the entire query, the system achieves accurate semantic matching with minimal computational overhead
3Measurement precision
If the search engine compares search results of modified queries with the original query, then the user intent prediction is improved, but the processing time increases
Solution Approach 1:
The system uses feedback from comparing search results to refine the semantic interpretation selection. By analyzing which modified queries produce results most similar to the original query, the system can iteratively improve its understanding of user intent and select the most accurate interpretation, thereby improving prediction accuracy through feedback-driven optimization
Solution Approach 2:
The system performs partial comparisons by focusing on key result features rather than exhaustively analyzing all search results. By comparing only the most relevant result characteristics (such as title, snippet, or top results) rather than every detail, the system achieves sufficient accuracy for intent prediction with significantly reduced processing time
Data Source
AI summary
The present disclosure relates to evaluating different semantic interpretations of a search query. One example method includes obtaining a set of search results for a particular search query submitted to a search engine; obtaining a set of semantic interpretations for the particular search query; obtaining, for each semantic interpretation of the set, a canonical search query; generating a modified search query based at least in part on the particular search query and the canonical search query for the semantic interpretation; obtaining a set of search results for the modified search query for the semantic interpretation; and determining, for each semantic interpretation of the set, a degree of similarity between (i) the set of search results of the modified search query for the semantic interpretation, and (ii) the set of search results for the particular search query.