Ontology Data Conflict Resolution for Source Updates and User Edits
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Solution Overview
Problem
Existing systems struggle to effectively manage conflicts between data source updates and user edits in ontology-based databases, leading to inefficiencies in data management and inconsistent user-centric views of data evolution.
Innovation Solution
A system is developed to prioritize user edits over data source updates, resolving conflicts through a predetermined strategy that ensures user-centric data evolution, thereby improving data quality and reducing network traffic by managing ontology data changes before updating databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data source updates are applied to ontology databases, then data freshness and completeness are improved, but user edit consistency and data quality deteriorate due to conflicts between automated updates and manual corrections
Solution Approach 1:
The system performs preliminary actions by detecting and resolving conflicts between data source updates and user edits before the updates are applied to the ontology database. This proactive conflict resolution ensures that user corrections are preserved while still incorporating valid data source updates, thereby maintaining both data freshness and user edit consistency.
Solution Approach 2:
The system implements a feedback mechanism that monitors the ontology database for conflicts arising from data source updates. When conflicts are detected, the system automatically resolves them by comparing update timestamps with user edit timestamps and applying appropriate resolution strategies. This continuous feedback loop ensures data quality is maintained while allowing automated updates to refresh the ontology.
2Quantity of substance
If all data source updates are incorporated into the ontology database, then data completeness is improved, but network traffic and processing overhead increase
Solution Approach 1:
The system extracts only the necessary conflict resolution information from data source updates rather than incorporating all updates directly into the ontology database. By identifying and resolving only those updates that conflict with user edits, the system reduces network traffic and processing overhead while still maintaining data completeness for non-conflicting updates.
Solution Approach 2:
The system applies partial action by selectively processing data source updates based on conflict detection results. Instead of applying all updates universally, the system applies updates only where they do not conflict with user edits or where conflict resolution rules permit, thereby reducing unnecessary network traffic and processing while maintaining data completeness where appropriate.
3Reliability
If conflict resolution prioritizes data source updates, then data freshness is improved, but user-centric data evolution and data quality deteriorate
Solution Approach 1:
The system changes the priority parameter dynamically based on the type of change being applied. For user edits, the system assigns higher priority to maintain user-centric data evolution, while for data source updates, it assigns appropriate priority based on update importance and conflict context. This parameter adjustment ensures data freshness is maintained without sacrificing data quality or user edit integrity.
4Manufacturing precision
If user edits are prioritized over data source updates, then user edit consistency is improved, but data completeness and freshness deteriorate
Solution Approach 1:
The system segments data source updates into two categories: those that conflict with user edits and those that do not. Non-conflicting updates are applied automatically to maintain data completeness and freshness, while conflicting updates are held for manual review or resolved using configured strategies. This segmentation allows user edit consistency to be maintained while still incorporating beneficial updates.
Solution Approach 2:
The system performs preliminary conflict detection and classification before applying data source updates. By identifying non-conflicting updates in advance, the system can apply them automatically without compromising user edit consistency, thereby maintaining data completeness while respecting user-centric data evolution priorities.
Data Source
AI summary
A system for resolving conflicts between data source updates and user edits to an ontology before applying the resulting changes to the ontology and related methods are disclosed. The system is programmed to receive data source updates from data sources and transform the data source updates to updates to ontology data. The system is also programmed to receive edits to ontology data from user accounts. The system is programmed to review these updates or edits to the ontology and resolve conflicts according to a predetermined strategy, such as prioritizing a user edit over a data source update. The resulting changes are incorporated to one or more databases where representations of the ontology data are stored.