Query Analysis System for Entity Attribute Ranking
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Solution Overview
Problem
Search engines face challenges in identifying and providing the most relevant and frequently requested information about different entities, as the type of information users are interested in varies significantly depending on the subject, and existing methods lack efficiency in analyzing user queries to determine such patterns.
Innovation Solution
The system analyzes user queries to identify entity-descriptive portions and suffixes, estimating query submission counts and assigning scores based on entity-level and type-level counts, ranking suffixes, and designating frequently requested facts for search results, utilizing human rater input to validate objective facts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the search engine provides comprehensive information about all entities, then the completeness of information is improved, but the complexity of processing and presenting information increases
Solution Approach 1:
The patent applies local quality by providing different types of information for different entities based on their specific characteristics. The system identifies entity types (person, organization, location, etc.) and provides tailored information attributes for each type, rather than treating all entities uniformly. This resolves the contradiction by making the information provision selective and targeted, improving completeness for relevant information while avoiding the complexity of processing all possible information for all entities.
Solution Approach 2:
The patent changes parameters by dynamically determining which information attributes to provide based on entity type, query context, and user needs. The system adjusts the set of information attributes presented by analyzing query patterns and entity characteristics, transforming the static approach of providing all information into a dynamic parameter-based selection process that balances completeness with processing efficiency.
2Measurement precision
If the search engine analyzes all query components in detail, then the precision of identifying user intent is improved, but the time required for query processing increases
Solution Approach 1:
The patent applies segmentation by dividing the query into distinct components: entity-descriptive portions and suffixes. The system separately analyzes the entity portion to identify the target entity and the suffix to determine the type of information requested. This segmentation allows for efficient processing by handling each component with appropriate analysis depth, improving precision without requiring exhaustive analysis of the entire query string.
Solution Approach 2:
The patent implements preliminary action by pre-processing and storing entity information and attributes in structured formats. When a query is received, the system can quickly retrieve and match against pre-analyzed entity data rather than performing complete analysis from scratch. This preliminary preparation enables fast query processing while maintaining high precision in identifying user intent through pre-computed entity attributes and relationships.
3Ease of operation
If the search engine provides frequently requested information proactively, then the user experience is improved, but the amount of data to be processed and stored increases
Solution Approach 1:
The patent applies preliminary action by pre-identifying and storing frequently requested information attributes for different entity types. The system analyzes historical query data to determine common information needs (such as address, phone number, hours of operation for businesses) and prepares this information in advance. When users query about entities of these types, the pre-prepared information is readily available for proactive presentation, improving user experience without requiring real-time computation of what information to provide.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query analysis. Queries are identified in query data, and an entity-descriptive portion and a suffix are determined in each query. Query counts are determined for a number of times that the respective queries occur in the query data. Based on the query counts, an entity-level count is estimated, which represents a number of query submissions that include the particular suffix and are considered to refer to a first entity. The entity is determined to be a particular type of entity. A type-level count is determined, which represents a number of query submissions that include the first suffix and are estimated to refer to entities of the particular type of entity. A score is assigned to the particular suffix based on the entity-level count and the type-level count.