Ontology Schema Refinement via Semantic Conflict Detection
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Solution Overview
Problem
Existing ontology schemas often fail to fully meet the specific requirements of application domains, leading to conflicts between the predefined schema and application data, requiring manual refinement which is costly and difficult to maintain dynamically.
Innovation Solution
An automated system and method that uses a rich context extractor to identify semantic relationship conflicts and an ontology schema refiner to transform relationship properties into new classes, enhancing the ontology schema with richer context to support varied application requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual refinement of ontology schema is performed to enhance fitness for application, then the ontology schema can support specific application requirements, but the cost and time consumption increase significantly
Solution Approach 1:
The system enables automatic ontology schema refinement by having the system refine its own ontology schema based on application data without requiring manual intervention. The automatic refiner extracts semantic relationships from application data and transforms the ontology schema accordingly, making the system self-sufficient in adapting to application requirements.
Solution Approach 2:
The patent replaces the manual mechanical process of ontology refinement with an automated computational system. The automatic refiner uses algorithms to extract semantic relationships from data and transform the ontology schema, substituting human expert manual work with an automated information processing system.
2Adaptability or versatility
If manual refinement of ontology schema is performed to resolve conflicts with application data, then the ontology schema can accommodate detailed information, but the difficulty of dynamic monitoring and maintenance increases
Solution Approach 1:
The system enables dynamic ontology schema refinement by continuously monitoring application data and automatically adjusting the ontology schema to reflect changes in the data. The automatic refiner can detect when data patterns change and refine the ontology schema accordingly, making the system adaptable to evolving requirements without manual intervention.
Solution Approach 2:
The system implements a feedback mechanism where the automatic refiner continuously monitors application data and uses this information to refine the ontology schema. The system learns from the data patterns and adjusts the ontology schema based on feedback from the application environment, enabling automatic adaptation to changing requirements.
3Adaptability or versatility
If generalized ontology schema is used to ensure wide applicability, then the schema can be widely applied across different applications, but it cannot fully capture specific application contexts and requirements
Solution Approach 1:
The system applies local quality by refining the ontology schema specifically for each application context while maintaining the general structure. The automatic refiner identifies application-specific semantic relationships and adds or modifies only the necessary elements to capture local context, rather than redesigning the entire ontology schema.
Solution Approach 2:
The system segments the ontology schema into general components that remain unchanged and application-specific components that are automatically refined. The automatic refiner works on specific portions of the ontology schema that are relevant to the application data, leaving the general structure intact while adding application-specific details.
Data Source
AI summary
The present invention provides a system and method for automatically refining ontology within a specific context. The system comprises: a rich context extractor for discovering a semantics relationship conflict existing between an original ontology schema and application data; and an ontology schema and mapping refiner for refining the original ontology schema based on the semantics relationship conflict discovered by the rich context extractor, and creating a new mapping between the refined ontology schema and a data schema of the application data. According to the invention, users can save a lot of work of manual refinement, since the ontology schema is automatically refined based on contexts in the application data.