Unified Semantic Ranking of Ontological Subjects
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Solution Overview
Problem
Current methods lack unified, systematic, and efficient ranking systems for ontological subjects across various orders and types of compositions, which are essential for applications like search engines, genomics, and signal processing.
Innovation Solution
A method and system that utilize Participation Matrices to rank ontological subjects of different orders by calculating Semantic Coverage Extent Number (SCEN) and Centrality Power Number (CPN), allowing for the evaluation of semantic importance and centrality within compositions, and combining these methods for unified ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ranking methods are used for ontological subjects, then implementation is simpler, but the system lacks unified and systematic ranking capability across various orders and types of compositions
Solution Approach 1:
The patent segments the ranking problem into hierarchical orders of ontological subjects (first-order, second-order, etc.), where each order represents a different level of composition. This segmentation allows the system to handle various types of compositions systematically by treating each order separately while maintaining unified ranking capabilities across all orders through consistent participation matrix construction and SCEN/CPN calculation methods.
2Measurement precision
If comprehensive ranking of all ontological subjects is performed, then completeness of analysis is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary construction of participation matrices that capture the relationships between ontological subjects at different orders before the actual ranking computation. These pre-computed participation matrices store the structural information needed for ranking, allowing the system to efficiently calculate SCEN and CPN values without re-processing the entire composition structure each time a ranking is needed, thus reducing processing time while maintaining comprehensive analysis.
3Measurement precision
If participation matrices and multiple ranking metrics are used, then ranking accuracy and semantic importance evaluation are improved, but computational complexity increases
Solution Approach 1:
The patent creates a universal participation matrix framework that serves multiple functions: it captures structural relationships between ontological subjects, enables calculation of both SCEN (Semantic Coverage Extent Number) and CPN (Centrality Power Number) metrics, and works across all orders of compositions. This multi-functional approach allows the system to achieve comprehensive ranking accuracy using a single unified mathematical structure rather than requiring separate complex mechanisms for each ranking aspect.
Data Source
AI summary
The present invention discloses methods, systems, and tools for unified semantic ranking of compositions of ontological subjects. The method breaks a composition into a plurality of partitions as well as its constituent ontological subjects of different orders and builds a participation matrix indicating the participation of ontological subjects of the composition in other ontological subjects, i.e. the partitions, of the composition. Using the participation information of the ontological subjects into each other a similarity matrix is built from which the semantic importance ranks of the partitions of the composition are calculated. The method, systematically, enables the calculation of the semantic ranks of the ontological subjects of different orders of the composition. Various systems for implementing the method and numerous applications and services are disclosed.