Ontology-Based Semantic Integration for Enterprise Data Interoperability
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Solution Overview
Problem
Current web service standards and semantic frameworks fail to address semantic mismatches between disparate information systems, leading to execution errors and lengthy integration cycles, as they lack the capability to automatically discover, integrate, and generate 'glue code' for web services and data repositories.
Innovation Solution
A method using semantic ontology management systems to generate executable code by linking domain ontologies of structured data repositories and web services, creating an expanded ontology to search for execution paths and automatically generate 'glue code' for data interoperability across the enterprise, leveraging OWL/RDF mappings and context ontologies to resolve semantic and contextual mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web service standards (HTTP, XML, WSDL, UDDI) are adopted for data exchange and service discovery, then interoperability between different systems is improved, but automatic service integration and code generation capabilities remain insufficient, requiring manual programming of glue code
Solution Approach 1:
The patent introduces an ontology-based intermediary layer that mediates between disparate web services and data repositories. This ontology serves as a semantic mediator that automatically maps concepts from different systems, enabling automated service composition and glue code generation without manual programming, while maintaining the interoperability benefits of standard web service protocols
Solution Approach 2:
The system enables web services and data repositories to self-describe their interfaces and data models through ontologies. This self-service capability allows the automated integration engine to discover, understand, and compose services automatically based on their own metadata, eliminating the need for manual glue code programming while preserving standard-based interoperability
2Ease of operation
If service-oriented architecture is implemented to make data repositories and web services available across the enterprise, then data accessibility is improved, but semantic mismatches between systems lead to execution errors and lengthy testing cycles
Solution Approach 1:
The patent performs preliminary semantic validation and mapping verification before service execution. By checking ontology compatibility and resolving semantic mismatches in advance, the system prevents execution errors from occurring, thereby maintaining high data accessibility while improving execution accuracy without requiring lengthy testing cycles
Solution Approach 2:
The system implements feedback mechanisms that monitor service execution and detect semantic mismatches in real-time. When mismatches are detected, the ontology-based integration engine automatically adjusts mappings and resolves conflicts, ensuring execution accuracy is maintained while preserving the ease of data accessibility across the enterprise
3Manufacturing precision
If manual programming of glue code is performed to integrate web services and data repositories, then integration precision can be controlled, but integration time and development complexity increase significantly
Solution Approach 1:
The patent performs preliminary automated ontology mapping and integration path planning before actual service composition. By pre-computing the integration logic based on ontological semantics, the system generates precise integration code automatically, achieving high integration precision without the time-consuming manual programming process
Solution Approach 2:
The system replaces the manual mechanical process of programming glue code with an automated ontology-based reasoning engine. This engine uses semantic relationships and logical inference to generate integration code automatically, substituting human programming effort with automated intelligent processing, thereby maintaining precision while dramatically reducing integration time
Data Source
AI summary
A system and method for integrating databases and/or web services into a searchable ontological structure. The structure allows free-form searching of the combined system, discovering an execution path through the ontology to provide answers to queries that may require accessing multiple systems to resolve, without a need for knowledge of the available databases and services or of query syntax by the user. The same technologies that integrate databases and web services into a single ontological structure may also provide interoperability between the numerous information systems within modern enterprises. Context ontologies are constructed to capture ubiquitous enterprise concepts and their representations across the enterprise. By mapping information system data models to these context ontologies, information that originates in one part of the enterprise may be used across the enterprise in a highly automated fashion.