Consolidating Dynamic Knowledge Organization Systems
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Solution Overview
Problem
Dynamic knowledge organization systems (KOS) face challenges in managing multiple versions, leading to increased storage space requirements and reduced usability due to the evolution of domain knowledge, where previous annotated data becomes difficult to use with updated ontologies.
Innovation Solution
A method for consolidating multiple versions of a KOS by identifying amended concepts, recording evolutionary relationships, and generating a consolidated hierarchical data structure that computes similarity values between concepts, allowing for efficient storage and improved searchability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple independent versions of the same ontology are kept to track knowledge evolution, then the comprehensive view of knowledge evolution is provided, but the storage space requirement increases significantly
Solution Approach 1:
The patent merges multiple ontology versions into a single consolidated structure by identifying amended concepts and recording evolutionary relationships. Instead of storing separate version files, the system integrates all versions into one unified ontology that preserves historical information through evolutionary links between concepts, thereby reducing storage space while maintaining complete knowledge evolution tracking.
Solution Approach 2:
The consolidated ontology serves multiple functions simultaneously: it acts as the current active ontology for data annotation, preserves historical versions for tracking evolution, and provides a comprehensive knowledge evolution view. This multi-functionality eliminates the need for separate storage of multiple independent version files.
2Adaptability or versatility
If the latest version of the ontology is used to keep pace with knowledge evolution, then up-to-date knowledge is maintained, but previously annotated data becomes difficult to use or useless
Solution Approach 1:
The system performs preliminary action by pre-establishing evolutionary relationships between concepts across different versions before data annotation conflicts arise. When concepts are amended, the system records the evolutionary links in advance, so that when querying annotated data, the system can automatically trace concept evolution and map old annotations to new concepts, ensuring continuous data usability.
Solution Approach 2:
The consolidated ontology structure acts as an intermediary between old and new concept versions. The evolutionary relationships serve as mediation mechanisms that connect annotated data from older versions with updated concepts in the latest version, allowing the system to maintain both knowledge adaptability and data reliability simultaneously.
3Loss of information
If multiple versions of ontology are stored independently, then version history is preserved, but the system complexity increases and searchability decreases
Solution Approach 1:
The patent combines multiple versioned ontologies into a single consolidated ontology structure that internally manages version history through evolutionary relationships. This merging approach preserves complete version history while reducing system complexity by eliminating the need to manage multiple independent version files and their interrelationships.
Data Source
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AI summary
The invention proposes a method for consolidating different versions of the same dynamic knowledge organization system. While reducing the storage space that is required for storing all the information comprised in the different versions, the method provides a data structure that provides access to rich hierarchical and evolutionary relationships in the underlying data.