Spherical Ontology Graph for Transdisciplinary Causality
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Solution Overview
Problem
Current systems fail to effectively quantify and provide purposeful information to users based on their roles and tasks, and struggle to offer transdisciplinary views of causality and emergence within organizational networks due to limitations in data representation and correlation from heterogeneous sources.
Innovation Solution
A system generating a digital representation of an organization network graph using ontological knowledge graphs, allowing for visual navigation and interrogation of data structures across multiple domains, enabling transdisciplinary structural and behavioral views of causality and emergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data representation systems are used, then data can be stored and accessed, but the systems fail to provide transdisciplinary views of causality and emergence within organizational networks
Solution Approach 1:
The patent transitions from traditional two-dimensional flat data representations to a three-dimensional spherical data structure. This dimensional change enables the system to simultaneously display multiple domains and their interrelationships, providing transdisciplinary views while preserving causality and emergence information that were lost in flat representations.
Solution Approach 2:
The patent implements a nested structure where multiple domain-specific data layers are contained within a unified spherical framework. Each layer represents a different domain (e.g., financial, operational, customer), and they are nested within the same visual space, allowing users to view both individual domains and their interconnections simultaneously.
2Adaptability or versatility
If comprehensive data from multiple domains is integrated, then transdisciplinary views are enabled, but the complexity of data representation and navigation increases
Solution Approach 1:
The patent creates a universal spherical data structure that can accommodate multiple domains and types of data simultaneously. This single multi-functional framework replaces the need for separate complex systems for each domain, simplifying navigation while maintaining comprehensive multi-domain integration capabilities.
Solution Approach 2:
The patent uses a spherical geometry to organize data, where the curved surface allows for intuitive spatial relationships between different domains. This geometric approach simplifies navigation compared to flat hierarchical structures, as users can visually comprehend relationships between distant data points through the spherical topology.
3Reliability
If detailed ontological knowledge graphs are generated, then causality and emergence are preserved, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary ontological analysis and knowledge graph generation during the data ingestion phase, before visualization is needed. By pre-processing and structuring the data into the spherical framework in advance, the system reduces real-time computational requirements while maintaining accurate causality representations.
Solution Approach 2:
The patent creates a simplified copy or abstraction of the complex ontological relationships within the spherical data structure. This copied representation preserves the essential causality and emergence patterns while requiring less computational power to manipulate and display than the full detailed knowledge graph.
Data Source
AI summary
System and methods obtain ontology and contextual data for a plurality of domains, and generate an ontological knowledge graph using the ontology and contextual data. The ontological knowledge graph includes a plurality of nodes with links between the plurality of nodes, wherein the plurality of nodes are contained within a spherically represented data structure. The plurality of nodes with the links are displayed in a curvilinear orthography world metaphor digital view within the spherically represented data structure.