Visualization Engine Ontology Knowledge Management
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Solution Overview
Problem
Current knowledge management systems are inadequate in effectively assimilating and visualizing vast amounts of information from various data sources, struggling with dynamic data changes and the need for static data encoding, while also providing accurate and complete knowledge representation.
Innovation Solution
A knowledge management system that utilizes a processor and memory to parse visualization requests, generate search queries, and submit them to an ontology, receiving results with instances and relationships, and then generates a visual representation using stored visualization rules, allowing for dynamic data classification and inference, and providing a user-friendly visualization of knowledge even with large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the ontology contains very large amounts of data and relationships, then the knowledge representation becomes comprehensive and accurate, but the visual representation becomes unmanageable and complex
Solution Approach 1:
The system segments the large ontology data into smaller visualizable units by applying visualization rules that selectively represent instances and relationships. The processor parses visualization requests to generate search queries that retrieve specific subsets of ontology data, transforming comprehensive but unmanageable ontology into manageable visual representations while preserving knowledge accuracy.
2Reliability
If the system encodes large amounts of static data and assertions into the ontology, then the knowledge base becomes comprehensive, but the system becomes rigid and unable to adapt to dynamic data changes
Solution Approach 1:
The system implements dynamics by allowing runtime grounding of symbols and resolution of concept relationships discovered in various data sources to data in the ontology. This enables the ontology to adapt to dynamic data changes without requiring re-encoding of static data, as the system can dynamically resolve relationships and classify newly discovered data based on its data signature.
3Loss of information
If the system provides detailed visual representation of all ontology data, then the knowledge completeness is maximized, but the user interface becomes overwhelming and difficult to navigate
Solution Approach 1:
The system applies partial action by using visualization rules to selectively represent only the necessary instances and relationships in the visual output. The processor parses visualization requests to generate search queries that retrieve specific subsets of ontology data, providing sufficient knowledge representation without overwhelming the user with complete ontology detail.
Data Source
AI summary
A system includes a memory operable to store visualization rules. The system also includes a processor communicatively coupled to the memory. The processor is operable to receive a visualization request relating to information stored in an ontology. The processor is further operable to parse the visualization request to generate a search query. The processor is further operable to submit the search query to the ontology. The processor is further operable to receive, in response to the query, a result. The result includes a plurality of instances and a plurality of relationships between the instances. The processor is further operable to generate a visual representation of the result using the visualization rules.