Information Retrieval System Query Refinement and Result Ranking
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Solution Overview
Problem
Conventional information-retrieval systems face challenges in processing poorly defined user queries, resulting in overwhelming search results that are time-consuming to sift through and often lack relevance.
Innovation Solution
An enhanced information-retrieval system comprising a database, server, preprocessing, switchboard, and post-processing modules that refine user queries, suggest alternatives, classify queries, select appropriate search engines, administer searches, and modify search results to improve relevance, using taxonomies, ontologies, and social feedback to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search engines return all matching documents, then the quantity of search results is maximized, but the time required to identify relevant documents increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing relevance weights, taxonomies, and ontologies before the actual search query is processed. This allows the search engine to quickly filter and rank results using pre-established criteria, reducing the time users need to sift through results while maintaining comprehensive result sets.
Solution Approach 2:
The patent replaces manual sifting and filtering of search results with automated computational mechanisms. The system uses algorithmic processing to automatically rank, filter, and organize search results based on multiple criteria including relevance weights, user profiles, and document metadata, eliminating the need for users to manually evaluate each result.
2Reliability
If the system performs comprehensive query analysis and result refinement, then the relevance of search results is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex information retrieval process into distinct functional modules: query analysis module, document retrieval module, relevance weighting module, and result ranking module. Each module handles a specific aspect of the search process, making the overall system more manageable and maintainable while achieving comprehensive result refinement through coordinated operation of these specialized components.
Solution Approach 2:
The patent introduces intermediary data structures including taxonomies, ontologies, and pre-computed relevance weights that mediate between the user's query and the final search results. These intermediaries translate user intent into structured criteria and transform raw document matches into ranked, relevant results, reducing the direct complexity of the query-processing relationship.
3Reliability
If the system applies multiple processing modules including preprocessing and postprocessing, then the quality of search results is enhanced, but the processing time increases
Solution Approach 1:
The system performs preliminary processing during system initialization and document indexing, creating pre-computed relevance weights, taxonomies, and ontologies before actual search queries are processed. This shifts processing workload from query-time to offline preparation, reducing the time impact of multiple processing modules during actual search operations while maintaining enhanced result quality.
Data Source
AI summary
The present inventors devised, among other things, systems, methods, and software for enhancing the relevancy of content presented to users in response to queries in an online information retrieval system. One exemplary system refines a user input query by making suggestions of alternatives queries. A switchboard module converts the refined query, administers one or more searches, and collects search results from one or more search engines based on the refined query. And, a post-processor module refines the collected search results by, for example, modifying the order of the results, removing inappropriate or undesirable content from the results, and/or applying historical performance analysis, based for example on social feedback.