Dynamic Modular Ontology for Conflict Resolution
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Solution Overview
Problem
Conventional ontologies are monolithic and static, making it difficult for organizations, especially large investigative ones, to easily share and maintain data models across different domains and teams, leading to redundant work and computational inefficiency.
Innovation Solution
A dynamic modular ontology system that allows for the creation of domain-specific data models using modular building blocks like data objects, properties, and links, enabling flexible definition and modification of ontologies, with support for inheritance and easy reuse of ontology modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic ontology is used to organize information, then the ontology provides a unified structure for data modeling, but it becomes difficult to share and maintain across different domains and teams, leading to redundant work
Solution Approach 1:
The patent divides a monolithic ontology into modular ontology components that can be independently developed, shared, and maintained. Each module represents a coherent domain or concept cluster that can be reused across multiple contexts without requiring coordination of the entire ontology structure.
Solution Approach 2:
The system performs preliminary actions by automatically detecting potential conflicts between ontology modules before they are merged or integrated. This advance detection allows teams to resolve issues before deployment, eliminating the need for complex manual coordination and reducing maintenance burden.
2Quantity of substance
If conventional ontologies are used in large investigative organizations with multiple domains of expertise, then comprehensive data coverage is achieved, but coordination and agreement among users on data modeling becomes difficult and time-consuming
Solution Approach 1:
The system provides self-service by automatically detecting conflicts between ontology modules without requiring manual intervention from ontology developers or domain experts. The automated conflict detection and reporting mechanism eliminates time-consuming coordination meetings and manual review processes.
Solution Approach 2:
The system implements feedback by automatically analyzing ontology module combinations and identifying potential conflicts before integration. This feedback mechanism guides developers in selecting compatible modules and resolving issues proactively, reducing coordination overhead.
3Productivity
If a monolithic ontology design is used, then a complete data model is provided, but reusing ontology definitions across different teams requires building ontologies from scratch, which is tedious and computationally expensive
Solution Approach 1:
The patent segments ontology definitions into reusable modules that can be independently developed and shared across teams. This modular structure allows teams to leverage existing ontology components rather than building complete ontologies from scratch, significantly improving productivity and ease of reuse.
4Adaptability or versatility
If ontology modules are combined from multiple sources, then comprehensive domain coverage is achieved, but conflicts in data type definitions may arise that require manual resolution
Solution Approach 1:
The system provides automated feedback by detecting conflicts in data type definitions when combining ontology modules from multiple sources. The conflict detection mechanism identifies inconsistent definitions and reports them to developers, enabling efficient resolution while maintaining comprehensive domain coverage.
Data Source
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AI summary
A system with methodology for dynamic modular ontology. In one embodiment, for example, a method comprises: receiving a command to create a new ontology module; receiving, a selection of a first ontology module to import into the new ontology module; receiving, a selection of a second ontology module to import into the new ontology module; detecting an ambiguous data type definition conflict between a first definition of a data type in the first ontology module and a second definition of the data type in the second ontology module; generating a third definition of the data type reflecting a resolution of the ambiguous data type definition conflict; and storing, in a data container, the new ontology module comprising the third definition of the data type.