Meta-Taxonomy Data Graph for Cross-Dataset Search
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Solution Overview
Problem
Information retrieval systems face inaccuracies and incompleteness due to disconnected taxonomies, leading to inefficient search results and recommendations, as different datasets use varying vocabularies and classifications, resulting in wasted computing resources and suboptimal user experiences.
Innovation Solution
A data management system harmonizes different taxonomies using a taxonomy harmonization model to generate a meta-taxonomy, creating connections between nodes in a data graph for efficient searching and recommendation generation, incorporating user profiles for personalized results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disconnected taxonomies are used to store data from different datasets, then each dataset can maintain its own vocabulary and classification system, but search accuracy and retrieval quality deteriorate due to inability to find relationships across different classification systems
Solution Approach 1:
The patent merges multiple disconnected taxonomies into a unified taxonomy structure that preserves the original vocabularies and classifications from different datasets while establishing relationships between them. This allows the system to maintain taxonomy diversity for adaptability while enabling cross-taxonomy search for accuracy.
Solution Approach 2:
The patent introduces an intermediary layer that maps and connects different taxonomy systems. This intermediary structure enables relationships to be established between classifications from different datasets without forcing a complete unification, thus preserving the original taxonomy characteristics while enabling accurate cross-dataset retrieval.
2Reliability
If comprehensive searches across all taxonomies are performed to ensure complete data retrieval, then retrieval completeness improves, but computing resource consumption increases due to redundant searches and computations
Solution Approach 1:
The patent performs preliminary actions by pre-establishing relationships between taxonomies and organizing data in a unified structure before search operations. This preliminary organization enables the system to retrieve complete data without performing redundant comprehensive searches across all disconnected taxonomies during actual query execution.
Solution Approach 2:
The patent segments the search process by allowing the system to navigate through the unified taxonomy structure efficiently. Instead of searching all taxonomies simultaneously, the segmented approach allows targeted traversal through related classifications, reducing redundant computations while maintaining retrieval completeness.
3Adaptability or versatility
If disconnected taxonomy systems are used to accommodate different datasets, then data organization flexibility improves, but recommendation quality deteriorates due to inability to generate personalized results across diverse content
Solution Approach 1:
The patent merges multiple taxonomy systems into a unified structure that maintains the organizational flexibility of individual taxonomies while enabling cross-taxonomy analysis. This unified structure allows the system to generate personalized recommendations by analyzing user interactions across all datasets regardless of their original classification systems.
Solution Approach 2:
The patent creates a universal taxonomy framework that serves multiple functions: maintaining original data organization flexibility while simultaneously enabling personalized recommendation generation. The unified structure allows the same system to accommodate diverse datasets and generate personalized recommendations across all of them.
Data Source
AI summary
In some implementations, a device may receive data associated with a set of taxonomies, wherein a taxonomy, of the set of taxonomies, represents a classification of a set of relationships of data entries of the data. The device may integrate the set of taxonomies to generate a meta-taxonomy of the data. The device may generate a data graph of the data based on integrating the set of taxonomies to generate the meta-taxonomy, wherein the data graph is based on a graph-based search model that is associated with at least one of: taxonomy-based filtering, metadata attribute prioritization, or semantic matching. The device may store the data graph.