Search Query Trigger Parsing for Intent-Based Result Refinement
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Solution Overview
Problem
Conventional search systems fail to utilize contextual cues within search queries, leading to irrelevant and excessive results, requiring additional computational resources and user effort.
Innovation Solution
A system that dynamically assigns fields to data content, parses search queries to identify triggers, and correlates them with relevant fields to match user intent, using configurable rules for structured search refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search systems use all words in a search query as keywords, then the search covers broad content, but the search results become irrelevant and excessive
Solution Approach 1:
The search query is segmented into different components: trigger words (indicating user intent) and keyword words (content terms). This segmentation allows the system to process trigger words for structural meaning and keyword words for content matching, thereby improving result relevance while filtering out excessive irrelevant results.
Solution Approach 2:
The system extracts trigger words from the search query that indicate user intent (such as temporal, spatial, or logical relationships). By taking out these trigger words and processing them separately through configurable rules, the system can structure the search more effectively and reduce irrelevant results.
2Reliability
If conventional search systems treat all search terms equally as keywords, then the search process is simple, but the search quality deteriorates
Solution Approach 1:
The system dynamically processes search queries by identifying trigger words and applying configurable rules based on the specific query context. Rather than using a static keyword-only approach, the system adapts its processing based on the detected trigger words, improving search quality while managing complexity through rule-based automation.
Solution Approach 2:
The system introduces an intermediary processing layer that analyzes search queries for trigger words and translates them into structured search operations. This intermediary layer bridges the simple keyword search and complex intent-based search, improving quality while keeping the core search engine relatively simple.
3Reliability
If conventional search systems return excessive results, then comprehensive coverage is achieved, but computational resources are wasted
Solution Approach 1:
The system performs preliminary processing of the search query to identify trigger words and structure the search before executing the actual search. By pre-processing the query to extract meaningful intent indicators, the system can focus computational resources on relevant searches and avoid processing excessive irrelevant results.
4Ease of operation
If conventional search systems require trial-and-error searches, then users can refine results, but user time is increased
Solution Approach 1:
The system automatically performs search refinement by identifying trigger words and applying configurable rules to structure the search. Instead of requiring users to manually refine searches through trial and error, the system self-adjusts the search based on the detected user intent, significantly reducing user time investment while maintaining ease of operation.
Data Source
AI summary
Provided are systems and methods for automatic search query refinement. An example method commences with identifying a plurality of electronic sources of data content of an entity stored at different network-accessible locations. The content may be dynamically assigned fields based on criteria specified by the entity. Thereupon, a unified search interface may be provided to authorized users to search the content. A search query subsequently received from a user may be parsed. The method continues with determining, upon the parsing and based on predetermined rules, triggers associated with the search query. In some embodiments, the triggers include search triggers to be used for searching content, filter triggers to be applied for filtering search results, and structural triggers to be used for ranking the search results. The method further includes searching the content based on the triggers to retrieve the search results and providing the search results to the user.