OPC UA Data Model Ontology Mapping for Automated Validation
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Solution Overview
Problem
Existing OPC UA data models lack formal semantic representation, leading to incomplete automatic validation, complex querying, and hindered analytical processes such as skill-matching and data mining.
Innovation Solution
Transforming the semantically enriched and graph-based OPC UA data model into a formal ontology using a computer-implemented method, where node identifications are converted to unique resource identifiers and semantic descriptions are mapped to predicates and classes in the target ontology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If OPC UA data model is used for semantic enrichment, then information exchange capability is improved, but formal semantic representation is lacking leading to validation and querying difficulties
Solution Approach 1:
The patent introduces an intermediary layer that translates OPC UA's informal semantic descriptions into formal ontology representations (RDF triples). This mediator enables automated validation and querying by converting the scattered semantic information into a structured format that reasoning tools can process, without changing the original OPC UA data model.
Solution Approach 2:
The patent transforms the representation parameters of semantic information by converting node identifications to URIs, semantic descriptions to predicates and classes, and organizing data as RDF triples. This parameter transformation enables the use of standard ontology validation and querying tools while preserving the original semantic meaning.
2Adaptability or versatility
If scattered semantic descriptions are used within OPC UA data model, then flexibility in data representation is improved, but automatic validation and analytical processing are hindered
Solution Approach 1:
The patent creates a formal ontology copy of the OPC UA data model's semantic information. This copy maintains the same semantic content but represents it in a structured RDF format that enables automated validation and analytical processing, while the original flexible OPC UA model remains unchanged.
Solution Approach 2:
The patent introduces a transformation intermediary that bridges the flexible but informal OPC UA semantic representations and the structured formal ontology. This intermediary enables automated processing by translating scattered semantic descriptions into a format suitable for machine validation and analysis.
3Extent of automation
If formal ontology transformation is applied to OPC UA data model, then automated validation and querying capability is improved, but processing overhead and transformation complexity increases
Solution Approach 1:
The patent segments the transformation process into distinct steps: extracting node identifications and converting them to URIs, retrieving and transforming semantic descriptions into predicates and classes, and organizing data as RDF triples. This segmentation makes the complex transformation process more manageable and implementable.
Solution Approach 2:
The patent enables the OPC UA data model to serve itself by automatically generating the formal ontology representation through systematic transformation of its own semantic information. The transformation process uses the existing OPC UA semantic descriptions to create the validation-ready ontology without requiring external manual intervention.
Data Source
AI summary
Providing data generally involves a transformation of an OPC UA data model into an ontology. Such an ontology readily provides desired capabilities for validation, querying, and analytics of the OPC UA data model using sophisticated standard tools adapted to the ontology. A method for transforming a semantically enriched and graph-based data model into a target ontology is suggested. By a first act, identifications of nodes are retrieved of the data model. Subsequently or concurrently, semantic descriptions of one or more references interconnecting one or more nodes are retrieved. These semantic descriptions are expressed by at least one or more predicates and one or more concepts (e.g., classes) in the target ontology to be produced. Eventually, the target ontology is structured by a semantic ontology language and output to a triple store.