Ontology Class Generation Using Instance Graph Communities
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Solution Overview
Problem
Manual ontology creation is time-consuming, prone to errors, and subject to terminology variation, leading to inconsistent and difficult-to-compare ontologies.
Innovation Solution
Automated processes using instance graphs to determine properties and generate class definitions for ontologies, including alignment of labels and application of graph decomposition techniques to identify relevant semantic concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual creation of ontologies is performed, then flexibility and customization are improved, but time consumption and error rate increase
Solution Approach 1:
The system automatically extracts properties, classes, and relationships from instance graphs without requiring manual intervention. The automated ontology creation process serves itself by utilizing computational algorithms to generate ontology structures, property definitions, and class hierarchies from unstructured or semi-structured data sources, eliminating the need for manual curation while maintaining adaptability through configurable parameters.
Solution Approach 2:
The patent replaces the manual mechanical process of ontology creation with an automated computational system. Instance graphs are processed through algorithmic operations that automatically identify entities, extract relationships, infer properties, and generate ontology structures. This substitution of manual labor with automated computational mechanisms dramatically reduces creation time while maintaining or improving consistency and accuracy.
2Reliability
If manual creation of ontologies is performed, then expert judgment can be applied, but terminology variation and inconsistency increase
Solution Approach 1:
The system transforms unstructured instance graph data into structured ontology components by applying parameter-based extraction rules. Configuration parameters control the extraction process, allowing adjustment of precision, recall, and granularity. The system maintains consistency by applying uniform parameter-based rules across all extracted entities, eliminating terminology variation while preserving expert-level quality through configurable extraction criteria.
Solution Approach 2:
The patent creates standardized ontology representations by copying and normalizing extracted information into consistent formats. Instance graphs are transformed into standardized ontology structures with uniform property schemas, data types, and relationship representations. This copying process ensures that all extracted information adheres to consistent formatting rules, eliminating terminology variation while maintaining the semantic content.
3Productivity
If automated ontology creation is implemented, then time consumption and error rate are reduced, but complexity of the process increases
Solution Approach 1:
The automated ontology creation process is divided into distinct modular stages: instance graph generation, property extraction, class identification, relationship inference, and ontology assembly. Each stage operates independently with defined inputs and outputs, allowing the complex overall process to be managed through manageable segments. This segmentation reduces process complexity by breaking down the automation into discrete, testable, and configurable components.
Solution Approach 2:
The patent introduces instance graphs as an intermediary representation between raw data and the final ontology structure. Instance graphs serve as a intermediate format that captures entities, properties, and relationships in a structured but flexible manner, facilitating the transformation process. This intermediary layer simplifies the automation by providing a standardized intermediate representation that can be processed through algorithmic operations before generating the final ontology.
4Manufacturing precision
If automated ontology creation is implemented, then consistency and standardization are improved, but flexibility in handling diverse data formats decreases
Solution Approach 1:
The system employs universal instance graph structures that can represent diverse data formats through a common schema. The instance graph format serves as a universal intermediary that can accommodate different input formats (JSON, XML, CSV, databases, unstructured text) by mapping them to a standardized graph representation. This universality allows the automated creation process to maintain high standardization in the output ontology while accepting flexible diverse inputs through the adaptable instance graph intermediate format.
Data Source
AI summary
Class definitions for an ontology of a domain are determined using a materialized instance graph, where the ontology is used for semantic query execution, automated analytical reasoning, or for machine learning. A plurality of instances graphs for a respective plurality of domain instances are received. A materialized instance graph is generated from the plurality of instance graphs. One or more communities represented in the materialized instance graph are determined. Properties associated with respective communities of the one or more communities are determined. Class definitions are generated, where a class corresponds to a community of the one or more communities and at least a portion of properties associated with the community. Class definitions are assigned to the ontology for the domain.