Extended Semantic Annotations for Generating Translators in the Arrowhead Framework

Moutinho, Filipe; Paiva, Luis; Köpke, Julius; Maló, Pedro

doi:10.1109/tii.2017.2780887

Cited by 15 publications

(40 citation statements)

References 25 publications

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“…This way it is possible, in principle, that the translator requests or provides additional information needed to proceed with a translation. For example, although the messages in Listing 1 refers to the same location, an external information source is needed to identify this relationship, for example as described in [21]. It should be noted that NLP translations of this type are currently challenging to learn.…”

Section: Discussion: Translator Learning Strategiesmentioning

confidence: 99%

“…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 [21], see also former work in [12]. That translator can map elements, perform unit conversion, detect missing data and, in certain cases, find and add the missing data.…”

Section: Related Work On Interoperability Solutionsmentioning

confidence: 99%

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Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Nilsson

Sandin

Delsing

2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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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.

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Section: Discussion: Translator Learning Strategiesmentioning

confidence: 99%

Section: Related Work On Interoperability Solutionsmentioning

confidence: 99%

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Nilsson

Sandin

Delsing

2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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“…The produced results are moderate and there is a space for enhancement with other incremental clustering techniques. Moutinho et al [28] have extended the semantic annotations for integrating XML-messages using generating translators under the domain of arrowhead framework. These annotations are not automatic and only domain specific.…”

Section: Background and Related Workmentioning

confidence: 99%

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Núñez‐Valdéz¹,

Solanki²,

Balakrishna³

et al. 2020

IJIMAI

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In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHC-AA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely-Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data.

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“…To dynamically support the semantic interoperability between heterogeneous systems, an approach was proposed in [4]. In this approach, it is verified if two systems can interact and, if possible, a translator is automatically generated.…”

Section: Introductionmentioning

confidence: 99%

“…These annotations, not only map the XML elements/attributes of the exchanged messages to the concepts/properties of a reference ontology, but also group them to solve ambiguities. This paper proposes a set of annotation rules to ensure proper/valid semantic annotations in the XML Schemas (XSD) that support the approach proposed in [4]. It was defined, in [4], how to create each semantic annotation; however, it was not defined how to ensure that the set of annotations in an XSD are valid, i.e.…”

Section: Introductionmentioning

confidence: 99%

Annotation Rules for XML Schemas with Grouped Semantic Annotations

Campos-Rebelo

Moutinho

Paiva

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

Self Cite

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To enable the Industrial Internet of Things (IIoT), it is required to ensure Machine-to-Machine communications. Systems/devices often use different communication protocols, standards, and data representation languages, which create interoperability challenges. This paper proposes a set of annotation rules for systems meta-data, to support the translation of data exchanged between heterogeneous systems. These rules must be followed to ensure the validity of systems meta-data (XML Schemas annotated with semantic annotations and group identifiers). The meta-data can then be used as input in tools to analyze data and semantic compatibility and generate translators.

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Extended Semantic Annotations for Generating Translators in the Arrowhead Framework

Cited by 15 publications

References 25 publications

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

Annotation Rules for XML Schemas with Grouped Semantic Annotations

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