Vector Embedding Optimization for Dynamic Social Network Taxonomy
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Solution Overview
Problem
Taxonomy data in social networks struggles to capture dynamic and subtle relationships between entities, as existing hierarchical structures fail to account for varying relationships relevant in different analyses, limiting their usefulness for predictive tasks.
Innovation Solution
A deep embedded representation system using machine learning techniques maps entities to vector representations, allowing for the optimization of embeddings to capture complex relationships and predict entity transitions, similarities, and relevance, bypassing the need for manual hierarchical taxonomies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If standardized taxonomy structures are used to organize entity attributes, then data organization and categorization are improved, but the ability to capture dynamic and subtle relationships between entities deteriorates
Solution Approach 1:
The patent transforms the flat, hierarchical taxonomy structure into a multi-dimensional vector space where entities are represented as vectors. This dimensional transformation allows relationships to be captured through vector operations (addition, subtraction, dot products) rather than fixed hierarchical paths, enabling dynamic relationship modeling while maintaining organizational structure.
Solution Approach 2:
The patent changes the parameter representation from discrete categorical values in a taxonomy to continuous vector embeddings. By representing entities as vectors with multiple dimensions, the system can capture subtle relationships through vector distance and direction, allowing dynamic relationship modeling while preserving the organizational benefits of taxonomy.
2Stability of the object's composition
If manual hierarchical taxonomies are created to represent entity relationships, then structured organization is achieved, but the complexity of manual creation and maintenance increases
Solution Approach 1:
The patent replaces the manual mechanical process of creating and maintaining hierarchical taxonomies with an automated machine learning system. The system automatically learns vector representations and relationships from data, eliminating the need for manual taxonomy construction while preserving structured organization through the learned vector space geometry.
Solution Approach 2:
The system performs self-service by automatically learning entity relationships and generating the vector space structure without human intervention. The machine learning model autonomously captures relationships from training data, maintaining and updating the organizational structure dynamically without requiring manual taxonomy management.
3Reliability
If categorical taxonomy data is used for predictive tasks, then data standardization is maintained, but the usefulness for capturing dynamic relationships deteriorates
Solution Approach 1:
The patent transforms categorical taxonomy parameters into continuous vector parameters through embedding. This parameter transformation enables the data to capture dynamic relationships and subtle patterns necessary for predictive tasks, while the vector representations maintain standardization through consistent dimensional structure and normalization.
Solution Approach 2:
By moving from one-dimensional categorical labels to multi-dimensional vector representations, the patent enables predictive capabilities through vector operations. The additional dimensions in the vector space capture nuanced relationships that single-dimensional categorical data cannot represent, improving predictive accuracy while maintaining data standardization.
Data Source
AI summary
In an example embodiment, for each of a plurality of different titles in a social network structure, the title is mapped into a first vector having n coordinates, while kills are mapped into a second vector having n coordinates. The first and second vectors are stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.