Entity-Based Search Query Rewriting for Recruitment
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Solution Overview
Problem
Traditional querying methods for social networks to find suitable job candidates are inefficient, requiring manual entry of search terms and multiple trials to identify qualified candidates, as they need to translate complex hiring position criteria into search queries, which can be time-consuming and prone to errors.
Innovation Solution
A system that uses a standardized entity taxonomy to identify and tag entities in search queries, allowing for query rewriting and result ranking, by mapping input terms to standardized entities and their confidence scores, thereby enhancing search precision and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual search term entry and multiple search trials are used to find suitable candidates, then search completeness can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing job descriptions to extract skills, qualifications, and requirements before the search process begins. It also pre-processes candidate profiles to structure their information. This preliminary structuring enables automated query generation without requiring multiple manual search trials, thus reducing time consumption while maintaining search completeness.
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between the job description and candidate database. This intermediary automatically translates job requirements into search queries using extracted skills and qualifications, eliminating the need for recruiters to manually create and iterate through multiple search queries. The intermediary processing layer resolves the contradiction by automating the translation process while preserving search accuracy.
2Measurement precision
If manual query creation is used to translate hiring criteria into search queries, then search precision can be maintained, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically extracting skills, qualifications, and requirements from job descriptions and using them to generate search queries without human intervention. The automated query generation system serves itself by translating hiring criteria into effective search queries, maintaining search precision while dramatically improving ease of operation. Recruiters simply need to input the job description, and the system handles the rest.
Solution Approach 2:
The patent replaces the mechanical process of manual query creation with an automated computational system. Instead of recruiters manually translating hiring criteria into search queries through cognitive effort and experience, an automated system performs this translation using text processing and pattern recognition. This substitution maintains search precision through systematic analysis while making the operation extremely easy by eliminating manual effort.
3Measurement precision
If experienced recruiters perform multiple searching trials to obtain satisfactory queries, then search accuracy can be improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs preliminary structuring of both job descriptions and candidate profiles before the search process. By pre-extracting skills, qualifications, and requirements from job descriptions and pre-structuring candidate information, the system eliminates the need for multiple iterative search trials. This preliminary preparation achieves high search accuracy in a single query generation step, reducing operational complexity while maintaining precision.
Solution Approach 2:
The patent introduces an intermediary automated query generation system that mediates between the job description input and the candidate database search. This intermediary automatically translates hiring criteria into optimized search queries using extracted skills and qualifications, replacing the complex iterative process performed by experienced recruiters. The intermediary layer simplifies the operational process while maintaining or improving search accuracy through systematic automated analysis.
Data Source
AI summary
In an example embodiment, one or more query terms are obtained. Then, for each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.