Dynamic Ontology Engine for Knowledge Management
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Solution Overview
Problem
Current knowledge management systems are inadequate in effectively assimilating and representing vast amounts of information from various data sources, often requiring large amounts of static data to be encoded and struggling with dynamic changes, inaccuracies, and incomplete data.
Innovation Solution
A knowledge management system that utilizes a dynamic ontology to validate and update knowledge assertions, allowing for the assimilation of data from any source, correction of inaccuracies, and inference of additional knowledge, while providing a visual representation of complex data through a search index and intelligent data agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a knowledge management system encodes large amounts of static data and assertions into the ontology, then the system can provide a comprehensive knowledge base, but the system becomes difficult to maintain and update when data constantly changes
Solution Approach 1:
The patent transforms the static ontology into a dynamic system by implementing automated update mechanisms. The knowledge management system continuously ingests new data sources, validates assertions against the ontology, and automatically updates the ontology structure and content without requiring manual re-encoding, thereby maintaining comprehensiveness while reducing maintenance complexity
Solution Approach 2:
The system implements self-service capabilities through automated validation and update processes. The knowledge management engine autonomously validates new assertions against existing ontology constraints, resolves conflicts, and updates the ontology without human intervention, allowing the system to maintain itself as data constantly changes
2Adaptability or versatility
If the system assimilates knowledge from multiple data sources with inaccuracies and incompleteness, then the knowledge base becomes more comprehensive, but the accuracy of knowledge representation decreases
Solution Approach 1:
The patent implements feedback mechanisms through validation rules and constraints that check new assertions against the existing ontology. The system provides feedback on assertion validity, identifies conflicts between data sources, and uses this feedback to resolve inaccuracies and maintain knowledge representation accuracy while assimilating diverse data sources
Solution Approach 2:
The system combines multiple data sources with different qualities and characteristics into a unified knowledge base. By layering validation rules, confidence scores, and source credibility metrics over the heterogeneous data, the system creates a composite knowledge structure that maintains accuracy while accommodating diverse inputs
3Quantity of substance
If the ontology contains very large amounts of data and relationships, then the knowledge base becomes more comprehensive, but visualizing the knowledge becomes unmanageable
Solution Approach 1:
The patent segments the large ontology into manageable visualizations by implementing filtering, grouping, and hierarchical display mechanisms. The system divides complex knowledge structures into smaller, organized segments that can be visualized effectively, allowing users to navigate and understand large amounts of data without being overwhelmed
Data Source
AI summary
A system includes a memory operable to store an ontology. The ontology includes a plurality of instances, and a plurality of relationships between the instances. The system also includes a processor communicatively coupled to the memory. The processor is operable to receive a proposed knowledge assertion. The proposed knowledge assertion includes a plurality of classified tokens and a plurality of relationships between the classified tokens. The processor is further operable to determine whether the classified tokens correspond to instances in the ontology. The processor is further operable to validate the proposed knowledge assertion based on the ontology. The processor is further operable to determine whether to update the ontology with the proposed knowledge assertion.