One of the functionalities that OpenEngSB provides is the ability to execute comprehensive queries over several data models and corresponding data at the same time. Due to that various consistency checks can be performed over the heterogeneous data sets coming from different engineering disciplines (e.g., mechanical, electrical and software engineering). However, currently an assistance of a Semantic Web expert is needed to transform the consistency checks (formulated in natural language by domain experts) into SPARQL queries. This impedes the acceptance of the platform by industrial users as they typically do not possess expertise with Semantic Web technology.
To address this problem we would like to investigate, what typical consistency checks (or parts of the checks) can be identified for industrial automation systems engineering. Based on these identified typical checks semantic querying templates – parameterized building blocks for constructing queries – will be formulated and expressed in SPARQL. The domain under consideration is automation systems engineering, where typically stakeholders coming from mechanical, electrical and software engineering have to collaborate. In the end the domain users will be able to choose from a catalogue of typical consistency checks, set specific values of corresponding parameters and automatically get the corresponding executable SPARQL query.

Benefit for the Student

Student will gain knowledge about different Semantic Web technologies, focusing in particular on querying over several models and SPARQL querying language; as well as about approaches for consistency checking in industrial automation systems engineering.

Benefit for the Project

Being able to automatically obtain executable SPARQL queries for a selected typical problem will help to hide the complexity of the Semantic Web technology from the user, thus facilitating the usage of the OpenEngSB by domain experts.


Experience in Java programming; basic knowledge of Semantic Web technologies (OWL, SPARQL) is desirable.


Richard Mordinyi, Marta Sabou

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