Interactive Keyword Optimization for Opaque Search Engines
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Solution Overview
Problem
Opaque search engines limit interactivity and efficiency in information retrieval due to their lack of transparency, leading to poor performance with ambiguous short keyword queries, especially in tasks like fake news detection where manual keyword definition restricts the number of claims that can be processed.
Innovation Solution
An automated interactive optimization method that collects labeled claims, estimates relevance using mean relevance error (MRE) based on word embeddings, and performs an interactive greedy search to iteratively select keywords for improving retrieval of relevant posts from opaque search engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual keyword definition is used for querying opaque search engines, then the number of claims that can be processed is limited, but the precision of keyword selection can be maintained
Solution Approach 1:
The system enables automatic keyword optimization where the search engine itself provides feedback signals (click-through rates, dwell time, bounce rates) that are used to iteratively refine keywords. This self-service mechanism eliminates manual keyword definition while processing large numbers of claims through automated feedback loops between query execution and keyword refinement.
Solution Approach 2:
The system implements feedback mechanisms by collecting user interaction data (click-through rates, dwell time, bounce rates) from search engine results and using this feedback to automatically optimize keywords. This closed-loop feedback system enables high-volume claim processing with dynamically adjusted keywords based on actual search performance.
2Ease of operation
If short keyword queries are used in opaque search engines, then the operation simplicity is maintained, but the retrieval performance deteriorates due to query ambiguity
Solution Approach 1:
The system performs preliminary keyword optimization before executing search queries by analyzing feedback signals from previous search operations. This preliminary action refines keywords to reduce ambiguity while maintaining simple query structures, thereby improving retrieval performance without compromising ease of operation.
Solution Approach 2:
The system makes keywords dynamic by continuously adjusting them based on real-time feedback from search engine results and user interactions. This dynamic keyword optimization allows the system to adapt to changing search contexts and reduce query ambiguity automatically, maintaining simple query interfaces while improving retrieval precision.
3Device complexity
If opaque search engines are used, then the system complexity is reduced, but the interactivity and information availability deteriorate
Solution Approach 1:
The system introduces an intermediary keyword optimization layer that operates between the user and the opaque search engine. This intermediary automatically refines keywords using feedback mechanisms, compensating for the lack of transparency in the search engine by providing intelligent keyword adjustment without requiring users to understand the underlying repository or algorithms.
Solution Approach 2:
The system enables the opaque search engine to self-optimize by automatically collecting and analyzing feedback signals (click-through rates, dwell time, bounce rates) to refine its own keyword performance. This self-service approach allows the system to improve information retrieval effectiveness without increasing user-facing complexity or transparency.
Data Source
AI summary
An automated interactive optimization method of short keyword queries for improving information retrieval from opaque (black box) search engines is disclosed. The method requires collecting data from several fact-checking websites, for creating dataset used for evaluation; estimating the relevance of posts/query results retrieved from a search engine to a given input document, by calculating the mean relevance error (MRE), based on estimating the minimal distance between words comprising both the retrieved posts and the input document; labeling a subset of claims for evaluation, by choosing a number of claims that gained the maximal and the minimal mean relevance error (MRE); and finding the most appropriate queries in order to retrieve the maximal number of relevant posts using an opaque search engine, by performing an interactive greedy search for the best word that should be added to the input query, for maximizing the corresponding posts retrieved by the search engine.