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Amazon Neptune is a graph database service that supports two graph models: W3C’s Resource Description Framework (RDF) and Labeled Property Graphs (LPG). Customers choose one or the other model. This choice determines which data modeling features can be used and – perhaps more importantly – which query languages are available. The choice between the two technology stacks is difficult and time consuming. It requires consideration of data modeling aspects, query language features, their adequacy for current and future use cases, as well as developer knowledge. Even in cases where customers evaluate the pros and cons and make a conscious choice that fits their use case, over time we often see requirements from new use cases emerge that could be addressed more easily with a different data model or query language. It is therefore highly desirable that the choice of the query language can be made without consideration of what graph model is chosen and can be easily revised or complemented at a later point. To this end, we advocate and explore the idea of OneGraph (“1G” for short), a single, unified graph data model that embraces both RDF and LPGs. The goal of 1G is to achieve interoperability at both data level, by supporting the co-existence of RDF and LPG in the same database, as well as query level, by enabling queries and updates over the unified data model with a query language of choice. In this paper, we sketch our vision and investigate technical challenges towards a unification of the two graph data models.

Cyber–physical systems (CPSs) in the manufacturing domain can be deployed to support monitoring and analysis of production systems of a factory in order to improve, support, or automate processes, such as maintenance or scheduling. When a network of CPS is subject to frequent changes, the semantic interoperability between the CPSs is of special interest in order to avoid manual, tedious, and error-prone information model alignments at runtime. Ontologies are a suitable technology to enable semantic interoperability, as they allow the building of information models that lank machine-readable meaning to information, thus enabling CPSs to mutually understand the shared information. The contribution of this article is twofold. First, we present an ontology building method that is tailored toward the needs of CPSs in the manufacturing domain. For this purpose, we introduce the requirements regarding this method and discuss related research concerning ontology building. The method itself is designed to begin with ontological requirements and to yield a formal ontology. As the reuse of ontologies and other information resources (IRs) is crucial to the success of ontology building projects, we put special emphasis on how to reuse IRs in the CPS domain. Second, we present a reusable set of ontology design patterns that have been developed with the aforementioned method in an industrial use case and illustrate their application in the considered industrial environment. The contribution of this article extends the method introduced, as a postconference paper, by a detailed industrial application. Note to Practitioners —With growing digitalization in industry, the exchange and use of manufacturing-related data are becoming increasingly important to improve, support, or automate processes. Thus, it is necessary to combine information from different data sources that have been designed by different vendors and may, therefore, be heterogeneous in structure and semantics. A system that plans a maintenance worker’s daily schedule, for instance, requires information about the status of machines, production plans, and inventory, which resides in other systems, such as programmable logic controllers (PLCs) or databases. When creating such information systems, accessing, searching, and understanding the different data sources is a time-intensive and error-prone procedure due to the heterogeneities of the data sources. Even worse, this procedure has to be repeated for every newly built system and for every newly introduced data source. To allow for eased access, searching, and understanding of these heterogeneous data sources, ontology can be used to integrate all heterogeneous data sources in one schema. This article contributes a method for building such ontologies in the manufacturing domain. Furthermore, a set of ontology design patterns is presented, which can be reused when building ontologies for a domain.