Ontology Alignment Composition via Mapping and Filtering Modules
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Solution Overview
Problem
Current ontology alignment methods are inefficient and inaccurate, especially in the medical domain, due to the complexity of concept and relation matrices, and fail to account for the linguistic structure of medical ontologies, leading to unfeasible solutions and inaccurate matches.
Innovation Solution
A modular method for composing ontology alignment using mapping and filtering functions that reflect linguistic features and incorporate user feedback, allowing for flexible and precise alignment by improving recall and precision values through the use of context-specific predicates and axioms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex structural methods are used for ontology alignment, then alignment accuracy is improved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The patent segments the ontology alignment process into multiple independent modules: concept matching module, relation matching module, and alignment composition module. Each module handles specific aspects of alignment independently, reducing the computational complexity of the overall system while maintaining accuracy through specialized processing at each stage.
Solution Approach 2:
The patent applies preliminary actions by pre-processing ontology concepts to extract linguistic features, generate concept representations, and prepare relation matrices before the actual alignment computation. This pre-processing step reduces the complexity of subsequent alignment operations by transforming raw ontology data into optimized representations that are easier and faster to process.
2Quantity of substance
If string-based methods are combined with structural methods, then alignment coverage is improved, but processing time increases
Solution Approach 1:
The patent merges string-based methods (for capturing linguistic similarities in concept labels) with structural methods (for analyzing ontology relationships and hierarchies) into a unified alignment framework. The concept matching module uses string-based approaches to identify potential matches, while the relation matching module uses structural analysis to verify and refine these matches, achieving comprehensive coverage through combined approaches.
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most critical alignment tasks rather than uniformly processing all aspects. The system prioritizes matching high-importance concepts and relations first, using less computationally intensive methods for preliminary filtering and reserving complex structural analysis for promising candidates, thereby reducing overall processing time while maintaining adequate coverage.
3Measurement precision
If iterative semi-automatic mapping is used, then alignment precision is improved, but operational complexity increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform alignment tasks with minimal human intervention. The automated alignment module independently executes concept matching, relation matching, and alignment composition without requiring manual guidance, thereby reducing operational complexity while maintaining high precision through sophisticated automated algorithms that learn from and adapt to domain-specific patterns.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously evaluates alignment results and adjusts its processing strategies accordingly. The evaluation module analyzes the quality of generated alignments and provides feedback to refine subsequent matching operations, creating an iterative improvement loop that enhances precision while the automated nature of feedback processing keeps operational complexity manageable.
Data Source
AI summary
A modular method of composing an ontology alignment provides a set of correspondences between at least two ontologies thereby allowing a composition of an optimal alignment by balancing a recall value and a precision of the alignment. A two-fold strategy is followed. By means of mapping functions a set of alignment correspondences is determined. Depending on a particular mapping function a recall value of the alignment can be improved by an extension of the set of correspondences. By filtering functions particularities of the domain are reflected and incorrect mappings are avoided. Depending on a particular filtering function a precision value of the alignment can be improved by restricting the set of correspondences.