Ontology Creation Assistance Device Using Template-Based Node Registration
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Solution Overview
Problem
Conventional ontology creation assistance devices require specialized metadata for associating data items with existing ontologies, leading to increased labor and time in creating accurate ontologies, especially as data scales up.
Innovation Solution
An ontology creation assistance device that searches an ontology database for node sets similar to a given word, creates a template using common properties and nodes, and registers new triples by connecting the template to a node with the given word, reducing the workload through automated triple creation and display-assisted correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual ontology creation is performed to ensure high accuracy, then ontology quality is improved, but the time and labor required increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically searching for similar nodes, extracting common properties, and creating template triples before the user finalizes ontology creation. This pre-processing reduces the time required while maintaining accuracy through user review of generated templates.
Solution Approach 2:
The system creates template triples by copying and generalizing patterns from existing similar nodes in the ontology database. These templates serve as reusable templates that can be applied to new nodes, significantly reducing creation time while maintaining consistency with existing ontology structures.
2Measurement precision
If specialized metadata is used for associating data items with existing ontologies, then association accuracy is improved, but the complexity of the system increases
Solution Approach 1:
The system extracts only the necessary common properties and node relationships from similar nodes to create templates, rather than requiring complete specialized metadata. This extraction approach maintains association accuracy by focusing on essential characteristics while reducing system complexity.
Solution Approach 2:
The template creation mechanism serves multiple functions: it automatically generates triple templates, suggests property associations, and provides a review interface. This multi-functionality reduces the need for separate specialized metadata processing systems, thereby reducing overall system complexity while maintaining accuracy.
3Quantity of substance
If the number of data items increases to improve knowledge base comprehensiveness, then knowledge coverage is improved, but the labor and time for selecting properties for each data item increase
Solution Approach 1:
The system copies property patterns from similar existing nodes to automatically generate template triples for new data items. This copying mechanism enables rapid processing of large numbers of data items without requiring manual property selection for each item, thereby maintaining productivity despite increased data volume.
Solution Approach 2:
The system changes the parameter of property selection from manual item-by-item processing to automated template-based processing. This parameter change in the selection process enables the system to handle increasing numbers of data items efficiently while maintaining comprehensive knowledge base coverage.
Data Source
AI summary
A search unit (13) searches an ontology database (11) for a node set similar to a given word. A template creating unit (14) creates a template from common properties and nodes possessed by nodes included in the node set that has been searched for. An additional information determining unit (15) creates a triple by connecting the template to a node having the given word as a name, uses the created triple as display data, and when a triple is given, registers the given triple in the ontology database (11).