Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.
The development of more potent interoperability methods and technologies are of central importance for modern SOA, like the aforementioned Arrowhead Framework. For example, ontology-based XML-message translation has been extended with semantic annotations @cite_30 , see also former work in @cite_4 . That translator can map elements, perform unit conversion, detect missing data and, in certain cases, find and add the missing data. Another example is the architecture for device management using autonomic computing @cite_32 , where a manager monitors and plans execution using ontologies and a reasoning engine.
@cite_30 @cite_4 @cite_32
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In order to create distributed automation systems, it is required to ensure their interoperability; however, ensuring interoperability between heterogeneous systems (using different communication protocols, data formats, and semantics) is a challenging task. Among many interoperability challenges, this paper addresses issues concerning semantic and data interoperability, namely, it provides a contribution to support the semantic compatibility verification and the generation of translators for XML messages. Translators are generated based on XML-schemas that are annotated with a reference ontology. We base our annotations on an extension of an existing declarative annotation method. In particular, our extension explicitly addresses ambiguities of annotations, schema instance mismatches, and coverage mismatches that frequently occur on Internet-of-things message schemas. We have evaluated our approach based on a set of interaction scenarios from the domain of the Arrowhead Project. A tool prototype that supports the semantic compatibility verification and the generation of XML translators is available at http: gres.uninova.pt tag . knowledge management systems enable the automatic discovery, sharing and reuse of structured data sources on the semantic web. With the emergence of multilingual ontologies, accessing knowledge across natural language barriers has become a pressing issue for the multilingual semantic web. In this paper, a semantic-oriented cross-lingual ontology mapping (SOCOM) framework is proposed to enhance interoperability of ontology-based systems that involve multilingual knowledge repositories. The contribution of cross-lingual ontology mapping is demonstrated in two use case scenarios. In addition, the notion of appropriate ontology label translation, as employed by the SOCOM framework, is examined in a cross-lingual ontology mapping experiment involving ontologies with a similar domain of interest but labelled in English and Chinese respectively. Preliminary evaluation results indicate the promise of the cross- lingual mapping approach used in the SOCOM framework, and suggest that the integrated appropriate ontology label translation mechanism is effective in the facilitation of monolingual matching techniques in cross-lingual ontology mapping scenarios. The approach towards Semantic Web Information Extraction (IE) presented here is implemented in KIM – a platform for semantic indexing, annotation, and retrieval. It combines IE based on the mature text engineering platform (GATE1) with Semantic Web-compliant knowledge representation and management. The cornerstone is automatic generation of named-entity (NE) annotations with class and instance references to a semantic repository. Simplistic upper-level ontology, providing detailed coverage of the most popular entity types (Person, Organization, Location, etc.; more than 250 classes) is designed and used. A knowledge base (KB) with de-facto exhaustive coverage of real-world entities of general importance is maintained, used, and constantly enriched. Extensions of the ontology and KB take care of handling all the lexical resources used for IE, most notable, instead of gazetteer lists, aliases of specific entities are kept together with them in the KB. A Semantic Gazetteer uses the KB to generate lookup annotations. Ontologyaware pattern-matching grammars allow precise class information to be handled via rules at the optimal level of generality. The grammars are used to recognize NE, with class and instance information referring to the KIM ontology and KB. Recognition of identity relations between the entities is used to unify their references to the KB. Based on the recognized NE, template relation construction is performed via grammar rules. As a result of the latter, the KB is being enriched with the recognized relations between entities. At the final phase of the IE process, previously unknown aliases and entities are being added to the KB with their specific types. Technologies for Industry-4.0 are evolving rapidly, and the term semantics is widely used. Various standardization groups claim that they provide mechanisms to express semantics or allow the integration of semantic information. The emerging data format AutomationML (as IEC 62714) proposes a role-based approach to encode semantics in engineering models and has already standardized fundamental engineering concepts as role classes. However, for concrete data processing tasks the currently standardized role classes are not sufficient to unambiguously express the meaning of various vendor or application specific concepts, while user-defined role classes rely on hand wired semantics encoded in dedicated software, e.g. importer exporters. Yet AutomationML system unit classes represent reusable engineering objects as relational models of AutomationML roles, attributes, interfaces, internal elements and links, which can be used to describe complex user-specific concepts. To enable an automatic machine interpretation of these unstandardized relational models, we transform AutomationML data to a formal and declarative semantic representation using the Web Ontology Language (OWL), and propose a rule mining approach to learn the intended meaning of user selected system unit classes, i.e. to identify the common relational structure shared by the selected engineering objects.