Unified Semantic Scoring of Ontological Subjects
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Solution Overview
Problem
Current methods lack unified, systematic, and efficient scoring/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 score ontological subjects of different orders by calculating Semantic Coverage Extent Number (SCEN) and Centrality Power Number (CPN), allowing for the ranking of compositions based on semantic importance, independent of language and syntactic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional scoring methods are used for ontological subjects, then language-specific and syntax-dependent processing is possible, but unified systematic scoring across different orders and types of compositions cannot be achieved
Solution Approach 1:
The patent applies universality by creating a participation matrix framework that handles multiple types of ontological subjects (words, sentences, paragraphs, documents, genomes, signals) through a single unified scoring system. The matrix structure universally represents participation relationships across different orders of compositions, enabling language-independent and syntax-independent scoring that works for textual, genetic, and signal processing applications simultaneously.
2Measurement precision
If comprehensive scoring of all ontological subject orders is performed, then complete semantic analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the composition into hierarchical orders (zeroth order basic units, first order compositions, second order compositions, etc.) and creating separate participation matrices for each order. This allows the system to process and score each level independently, maintaining comprehensive semantic analysis while reducing computational complexity through modular processing of segmented components.
3Adaptability or versatility
If language-specific scoring methods are used, then linguistic nuances are captured, but language independence and broad applicability are lost
Solution Approach 1:
The patent replaces language-specific mechanical processing (syntax rules, linguistic analysis) with a mathematical matrix-based system. The participation matrices capture semantic relationships through numerical representations of participation, eliminating dependence on language-specific rules while preserving semantic information through the mathematical structure that represents participation patterns across all ontological subject orders.
Data Source
AI summary
The present invention discloses methods, systems, and tools for unified semantic scoring 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. The method, systematically, enables the calculation of the semantic scores/ranks, value significances of ontological subjects of different orders and/or calculating and obtaining adjacency data of their visual graphical representations, and/or the association strengths between the ontological subjects of different orders of the composition. Various systems for implementing the method and numerous applications and services are disclosed.