Search Entity Identification via Attribute Segmentation
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Solution Overview
Problem
Current search systems fail to effectively identify entities that match features or attributes named in user queries, such as medical conditions associated with symptoms or movies featuring specific actors, as they lack the capability to interpret implicit questions and provide relevant entity names directly in search results.
Innovation Solution
The system determines if a search query includes predetermined attributes, generates combined search queries with entity names, and uses the results to select and present relevant entity names, adjusting scores based on inverse document frequency and user interactions to refine search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the search system returns only traditional search results, then the system is simple to operate, but it cannot effectively identify and present entity names that match features or attributes in user queries
Solution Approach 1:
The search system is segmented into multiple independent components: a query analyzer that identifies attributes in user queries, an entity matcher that compares attributes against entity databases, and a result generator that presents entity names. This segmentation allows the system to add sophisticated entity identification capabilities without overwhelming complexity, as each component handles a specific task independently.
Solution Approach 2:
An intermediary entity matching module is introduced between the traditional search system and the user interface. This intermediary analyzes search results, identifies entities that match query attributes, and presents them separately from traditional search results. This mediator layer enables enhanced entity identification without fundamentally redesigning the entire search system.
2Measurement precision
If the system generates combined search queries for each entity to improve entity identification, then entity matching accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary analysis of the user query to identify key attributes and potential entities before generating combined search queries. By pre-processing the query to extract relevant attributes and filter potential entity candidates, the system reduces the number of combined queries needed and avoids unnecessary search operations, thereby reducing time loss while maintaining accuracy.
Solution Approach 2:
The system generates combined search queries for only the most promising entity candidates identified through preliminary analysis, rather than for all possible entities. This partial action approach focuses computational resources on the most likely matches, achieving high entity identification accuracy without the excessive time cost of exhaustively querying all entities in the database.
3Ease of operation
If the system presents entity names separately from search results, then entity identification becomes more prominent and useful, but the user interface complexity increases
Solution Approach 1:
The system extracts entity names from the traditional search results and presents them in a separate, dedicated section of the user interface. This extraction allows users to quickly identify relevant entities without sifting through unrelated search results, improving ease of operation. The separation maintains interface simplicity by clearly distinguishing between entity identification results and traditional search results.
Solution Approach 2:
The user interface is designed with different presentation qualities for different types of information: entity names are presented with prominent formatting and positioning in a dedicated section, while traditional search results maintain their conventional presentation. This local quality differentiation helps users quickly identify and access entity information without overwhelming the entire interface with complex formatting changes.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying entities using search results. One of the methods includes determining that a first search query includes a respective text reference to each of one or more predetermined attributes, wherein each attribute is associated with a first entity type; obtaining search results for the first search query from a search engine, the search results identifying a plurality of resources; identifying entities of the first entity type that are related to any of the plurality of resources identified by the search results; and selecting names of one or more of the identified entities of the first entity type to include in a response to the first search query.