Implicit Query Generation via Named Entity Extraction
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Solution Overview
Problem
Conventional search engines require explicit user queries to access relevant information, often leading to missed opportunities for users who are unaware of or forget information stored locally or globally that is relevant to their current context.
Innovation Solution
A system and method that identifies named entities from user interactions and contextual data to generate implicit search queries, combining local and global indices to provide relevant information without user intervention, using a query system that extracts keywords and ranks results based on user behavior and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional search engines require explicit user queries, then search results can be precise and relevant, but users may miss relevant information they are unaware of or forget
Solution Approach 1:
The system performs preliminary actions by continuously monitoring user interactions and contextual data to pre-identify named entities and generate implicit search queries before the user explicitly searches. This allows relevant information to be prepared and accessible in advance, resolving the contradiction by providing information proactively without requiring explicit user queries.
Solution Approach 2:
The search system performs self-service by automatically generating and executing search queries based on extracted named entities from user context, eliminating the need for manual user query input. The system serves itself by autonomously identifying information needs and retrieving relevant data, thus reducing user effort while maintaining information accessibility.
2Quantity of substance
If the system combines local and global indices, then information retrieval completeness improves, but system complexity increases
Solution Approach 1:
The system segments the information retrieval task by maintaining separate local and global indices for different types of information. The local index stores user-specific contextual data while the global index contains broader information, allowing the system to manage large quantities of information through structured segmentation rather than a single complex unified index.
Solution Approach 2:
The system merges the local and global indices during the query execution phase to provide comprehensive search results. By combining results from both indices, the system achieves complete information retrieval while managing complexity through modular integration rather than creating a single monolithic index structure.
3Productivity
If the system generates implicit search queries automatically, then information retrieval efficiency improves, but accuracy of query relevance may decrease
Solution Approach 1:
The system incorporates feedback mechanisms that monitor user interactions with search results and adjust the implicit query generation process accordingly. By analyzing user behavior patterns and result engagement, the system refines its named entity extraction and query formulation, improving relevance accuracy while maintaining high retrieval efficiency through automated operation.
Data Source
AI summary
Systems and methods for identifying a named entity are described. In one described system, a computer program, such as a query system, identifies an event associated with an article, identifies a named entity within the event, and creates an implicit search query comprising the named entity.