Query Analyzer Ontology Segmentation
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Solution Overview
Problem
Users often receive irrelevant search results due to poor query structure, misspelled words, or misinterpretation by the underlying system, leading to user frustration and reduced website visits.
Innovation Solution
A query analyzer that dissects queries by identifying critical words, assigning parts of speech and concepts, and allowing users to modify these components through drop-down menus, using ontological thresholds and machine-learning algorithms to improve query relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system provides automated query analysis and repair tools, then query relevance improves, but device complexity increases
Solution Approach 1:
The query analysis system is divided into distinct functional modules: a disjunction module that separates query components, an identifier module that identifies critical words, a determination module that evaluates ontological thresholds, and a presentation module that displays results. This segmentation allows each module to handle specific tasks independently, improving query analysis precision while managing system complexity through modular design.
Solution Approach 2:
The system introduces an intermediary query analyzer appliance that acts as a mediator between the user's initial query and the search engine. This intermediary dissects the query, identifies issues such as misspelled words or poor structure, and presents repair options to the user before the final search is executed, thereby improving query relevance without requiring direct modification of the core search engine.
2Ease of operation
If the system provides detailed query dissection and repair options, then ease of operation improves, but device complexity increases
Solution Approach 1:
The query analyzer empowers users to self-diagnose and self-repair their queries by presenting them with the dissected query components and allowing them to modify words, parts of speech, or concepts directly. The system provides the tools and information needed for users to improve their own queries without requiring complex automated repair algorithms, thereby enhancing ease of operation while keeping the interface manageable.
3Measurement precision
If the system filters words based on ontological thresholds, then measurement precision improves, but loss of information increases
Solution Approach 1:
The determination module applies ontological thresholds selectively to different words in the query based on their individual characteristics and importance. Rather than applying a uniform filter to all words, the system evaluates each word's contribution to the query's meaning and applies threshold-based filtering locally, thereby maintaining measurement precision for critical words while preserving contextual information through selective retention.
Data Source
AI summary
Methods for analyzing a query is provided. Methods may include receiving a query from a user. Methods may include dissecting the query into a plurality of word segments. Methods may include determining, based on a predetermined ontology, which of the plurality of words satisfies a predetermined ontological threshold and which words do not satisfy the predetermined ontological threshold. Methods may include discarding the words that do not satisfy the predetermined ontological threshold. Methods may include determining a part of speech associated with each word based on the content of the query. Methods may include determining a concept associated with each word based on the content of the query. Methods may include displaying to the user word that satisfies the predetermined threshold alongside the associated part of speech and associated concept. Methods may include enabling the user to change each word, each concept and each part of speech.