Automated Data Alignment System Using Ontology Extraction
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Solution Overview
Problem
Existing data alignment methods require manual intervention, which is time-consuming and resource-intensive, making it difficult to quickly integrate new data sets with existing data across multiple databases, especially in dynamic environments where new data sets are frequently added.
Innovation Solution
An automated data alignment system using a multi-agent cognition system that extracts data models from new data sets, determines ontology assertions, and modifies existing ontologies to create a unified schema, leveraging OWL/RDF standards for machine-interpretable representations and logical inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment methods are used to integrate new data sets with existing databases, then alignment accuracy can be maintained through human expertise, but alignment time and resource consumption increase significantly
Solution Approach 1:
The patent introduces an automated alignment system that acts as an intermediary between new data sets and existing databases. This system uses machine learning models and ontology-based approaches to automatically match schema elements, data formats, and semantic meanings without requiring manual human intervention for each alignment task, thereby reducing time loss while maintaining accuracy through algorithmic precision
Solution Approach 2:
The patent replaces the mechanical manual process of data alignment with an automated computational system. The system uses automated schema comparison algorithms, machine learning-based matching, and programmatic ontology integration to substitute human manual operations, dramatically reducing alignment time from days or weeks to minutes or hours while maintaining or improving alignment quality through consistent automated procedures
2Reliability
If manual alignment methods are used to integrate new data sets, then data quality can be controlled through human review, but resource consumption and operational complexity increase
Solution Approach 1:
The patent implements a self-service automated alignment system that autonomously performs schema matching, data format conversion, and ontology integration without requiring human operators. The system includes self-validation mechanisms, automated conflict resolution, and built-in quality assurance through consistency checks, thereby maintaining data quality while eliminating the operational complexity of manual coordination and human resource management
Solution Approach 2:
The patent changes the operational parameters from manual human-controlled processes to automated computational processes with adjustable algorithmic parameters. The system allows configuration of matching thresholds, ontology schemas, and validation rules as programmable parameters rather than requiring human judgment, reducing operational complexity while maintaining or improving data quality through consistent application of defined parameters
3Productivity
If automated alignment systems are implemented to reduce alignment time, then productivity increases, but system complexity and initial resource requirements increase
Solution Approach 1:
The patent segments the automated alignment system into modular functional components: schema extraction modules, ontology matching modules, data format conversion modules, and validation modules. Each module performs a specific function independently, allowing the system to achieve high productivity through parallel processing while managing complexity through modular design that enables independent development, testing, and maintenance of each component
Solution Approach 2:
The patent implements a universal automated alignment system that can handle multiple data formats, schema types, and ontology standards through a single integrated platform. The system uses format-agnostic schema extraction, multi-language ontology support, and adaptable matching algorithms that work across different database types, thereby achieving high productivity across diverse data sources without requiring separate specialized systems for each data type, thus managing overall system complexity
Data Source
AI summary
Methods, systems, and computer-readable media relating to adapting existing data based on one or more new sets of data are provided. An exemplary method comprises extracting a data model from a new data set. The method further comprises determining one or more ontology assertions based on the extracted data model and modifying at least one existing ontology based on the one or more ontology assertions.