Unified Ontology Query Interface for Multi-Database Access
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Solution Overview
Problem
Existing systems struggle to efficiently manage and query ontology-based databases across multiple formats, handle conflicts between data source updates and user edits, and enforce access control while maintaining versioning consistency.
Innovation Solution
A system is developed to provide a unified query interface across multiple ontology-based databases by selecting appropriate databases based on function type and ontology entity type, managing ontology entities in different forms, resolving conflicts by prioritizing user edits, and controlling access at various levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple ontology-based databases are used to store data in different formats, then data organization and accessibility are improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer (unified query interface/API gateway) between the user and multiple ontology databases. This intermediary translates high-level queries into database-specific operations, managing the complexity of interacting with multiple databases in different formats without exposing this complexity to users.
Solution Approach 2:
The unified query interface provides universal access to multiple ontology databases through a single standardized interface. It can handle queries across different database types (RDF, OWL, SPARQL, etc.) and formats, making the system adaptable to various data organization needs while presenting a consistent interface.
2Quantity of substance
If data from multiple sources is integrated into ontology databases, then data completeness is improved, but conflict resolution complexity increases
Solution Approach 1:
The system performs preliminary actions by establishing conflict resolution rules and priorities before conflicts occur. It pre-defines how to handle contradictions between data sources (e.g., source priority, timestamp-based resolution, user-defined rules), so when conflicts arise, they can be resolved automatically without complex real-time decision-making.
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors data conflicts between sources and automatically applies resolution strategies. The system provides feedback loops that track conflict patterns and adjust resolution approaches, reducing the perceived complexity by automating the resolution process.
3Reliability
If access control is enforced at multiple levels, then data security is improved, but operation complexity increases
Solution Approach 1:
The access control system is segmented into multiple hierarchical levels (ontology level, entity level, property level). Each level has its own access control policies and permissions, allowing fine-grained security without requiring complex global access control logic. Users can be granted access at specific levels without needing to understand the entire system architecture.
4Stability of the object's composition
If versioning is maintained across multiple databases, then data consistency is improved, but processing overhead increases
Solution Approach 1:
The system uses copying mechanisms to maintain versioning across databases. Instead of synchronizing all databases in real-time, it creates copies of ontology entities at different version points. This allows versioning consistency to be maintained with reduced processing overhead, as changes are propagated to copies rather than requiring continuous coordination between all databases.
Data Source
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AI summary
A system for providing a unified query interface across multiple ontology-based databases and related methods are disclosed. The system is programmed to receive calls to an application programming interface for querying an ontology, where ontology data is represented in different databases respectively in different forms. For each function being called, system is programmed to select a database from the different databases based on the function type and each ontology entity type being processed by the function. The system is further programmed to retrieve data from each selected database, merge the retrieval results as appropriate, and transform the final result to ontology data in response to the calls.