Expert-Informed Search Engine Using Adaptive Torrent-Based Heterogeneous Network
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Solution Overview
Problem
Traditional web-search engines rely heavily on automated algorithms that fail to differentiate semantic contexts and perform semantic analysis, leading to irrelevant search results due to the lack of human knowledge and contextual understanding, resulting in users receiving unsuitable information.
Innovation Solution
A system that utilizes human-knowledge based tagging and identification of web documents through community membership, contextual usage, social media connectivity, ratings, and emotions, allowing users to create customized tags and prioritize search results based on user-defined lexicons and interaction data, which includes weighing user-defined tags based on frequency of interaction and trust criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automated algorithms are used for web search, then search speed and coverage are improved, but semantic understanding and relevance accuracy deteriorate
Solution Approach 1:
The patent introduces user-defined tags and expert knowledge as intermediary elements between the search query and the document corpus. These tags serve as semantic mediators that bridge the gap between automated keyword matching and human understanding, allowing the system to maintain fast automated processing while improving semantic relevance through curated metadata.
Solution Approach 2:
The system performs preliminary tagging and categorization of documents by experts before the actual search operation. User-defined tags and expert knowledge are pre-attached to documents, creating a prepared semantic framework that enables rapid yet accurate search results without requiring complex real-time semantic analysis.
2Measurement precision
If user-defined tags and expert knowledge are integrated, then search relevance and personalization are improved, but system complexity increases
Solution Approach 1:
The patent segments the search system into distinct functional modules: a tagging module for user-defined tags, an expert knowledge base, a weighting module for interaction data, and a search execution module. This segmentation allows each component to handle specific tasks independently, managing overall system complexity through modular architecture while maintaining high search relevance.
Solution Approach 2:
The system employs a universal tagging framework that serves multiple functions: it enables user-defined categorization, incorporates expert knowledge, weights interaction data, and facilitates personalized search. This multi-functional tag system reduces the need for separate mechanisms for each function, thereby managing complexity while achieving diverse goals.
3Measurement precision
If interaction data is weighed based on frequency and trust criteria, then personalized search results are improved, but processing time increases
Solution Approach 1:
The system pre-calculates and stores weighting factors for interaction data based on frequency of interaction and trust criteria during user activities. These pre-computed weights are cached and readily available during search operations, eliminating the need for real-time calculation of trust metrics and significantly reducing processing time while maintaining personalization accuracy.
Solution Approach 2:
The system automatically updates and maintains the weighting of interaction data based on user behavior patterns without requiring manual intervention. Trust criteria and frequency metrics are self-updated as users interact with the system, enabling continuous personalization improvement without additional processing overhead during search operations.
Data Source
AI summary
A system for operating an expert-informed information acquisition engine utilizing an adaptive torrent-based heterogeneous network solution includes a memory storing computer-executable instructions; at least one processor configured to access the at least one memory and execute the computer-executable instructions to: receive user-defined tags associated with a first user; access interaction data associated with one or more second users and the first user; receive user-defined tags associated with one or more second users; identify a lexicon based on the user-defined tags; receive a query from the first user, wherein the query; rank one or more documents based on the lexicon; and display one or more documents based on the lexicon, with already-seen results removed.