Cognitive Search Query Validation Using Domain Language Detection Models
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Solution Overview
Problem
Conventional search engines fail to provide meaningful search results when queries contain grammatical errors or lack clear meaning due to missing information, as they lack the ability to semantically validate queries beyond syntactic corrections.
Innovation Solution
A method involving the generation of domain language detection models, which classify query intents using hierarchies of classification labels, allowing for the formulation of semantically valid alternative queries that accurately reflect user intent, thereby improving search result relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines perform automatic grammar correction and syntactic validation, then queries with grammatical errors can be corrected, but queries without syntactic errors but with unclear meaning cannot be validated and return meaningless results
Solution Approach 1:
The patent segments the query validation process into multiple independent components: syntactic validation (grammar checking) and semantic validation (meaning detection). Each component operates independently with its own rules and algorithms, allowing the system to handle different types of query errors through specialized modules rather than a monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary semantic validation layer between the user query and the search engine. This intermediary component analyzes query meaning using natural language processing techniques, classification models, and context understanding to determine whether a query has clear intent before passing it to the search engine, thus preventing meaningless searches without requiring complete system redesign.
2Measurement precision
If search engines perform semantic validation of queries to ensure clear meaning, then meaningless queries can be detected and corrected, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial semantic validation by focusing on detecting only the most critical cases of meaningless queries using efficient heuristics and rules. Rather than performing complete semantic analysis on every query, the system applies lightweight validation first and only performs deeper analysis when necessary, thus achieving adequate detection accuracy without excessive processing time for all queries.
Solution Approach 2:
The patent performs preliminary semantic checks using fast rules-based methods before committing to more computationally intensive analysis. By applying quick filters and common-sense validation rules first, the system can reject obviously meaningless queries immediately and reserve deeper semantic processing only for queries that require more sophisticated analysis, reducing overall processing time.
3Ease of operation
If search engines provide only syntactic corrections for queries, then grammatical errors can be fixed, but users lack guidance on proper term usage and query formulation in specific domains
Solution Approach 1:
The patent implements a feedback mechanism where the semantic validation system analyzes user queries, detects issues with terminology or formulation, and provides constructive suggestions for improvement. The system learns from user interactions and query patterns to offer increasingly accurate guidance on domain-specific term usage, helping users formulate better queries over time while preserving domain knowledge.
Solution Approach 2:
The patent enables users to self-correct their queries by providing them with semantic validation results and suggested improvements. Rather than requiring expert intervention or extensive pre-programming of domain knowledge, the system empowers users to understand and fix their own query issues using the feedback provided, reducing the need for manual domain expertise encoding.
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: generating domain language detection models respective to a plurality of domains. A query is mapped to a domain language detection model and intent of the query is determined by use of classification labels of the domain language detection model. Based on the classification labels that may be identified as the intent of the query, alternative queries are formed to be meaningful valid and are produced to a user.