Ontologies provide engineers and developers with an unambiguous, verifiable, and expandable knowledge base related to a certain domain. This ontology has been tested in different case studies to prove its capabilities to infer useful information about the COVID-19 pandemic. The resulting pandemic ontology provides a simple repository for storing information about the COVID-19, reusing existing ontologies, to offer multiple views about the disease, including the social context. The ontological engineering process defines tasks of fusion, alignment, and linking for integrating the ontologies. In this context, this research proposes a new ontology, called COVID-19 Pandemic ontology, which is the product of an ontological engineering process proposed in this research that allows the integration of several ontologies to cover all the aspects of this infectious disease. Thus, due to the large quantity of COVID-19 information in the form of ontologies, approaches to ontology integration and interoperability could be beneficial. Also, there are previous ontologies from other disciplines that can help to analyze the COVID-19 pandemic. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance.ĬOVID-19 has generated a lot of information in different formats, and one of them is in the ontology format. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Finally, we present SOMM-a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors.
Ontologies are often used in industry to capture the conceptual information models underpinning applications. This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. A second part explains some architecture and implementation background and how new plugins can be integrated into the common platform.
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In the chapter, we will present the feature set of the NeOn Toolkit and how to use it. Its user interface, especially the presentation of class restrictions, makes the NeOn Toolkit accessible to users that do not have long experience with ontologies but instead know the object-oriented modeling paradigm. The NeOn Toolkit supports the Web Ontology Language OWL 2, the ontology language specified by the W3C, and features basic editing and visualization functionality.
A substantial number of plugins have been developed within and outside the NeOn consortium and are available at the NeOn Toolkit homepage.
It is based on an open and modular plugin architecture that allows adding additional plugins realizing more advanced features supporting more complex ontology engineering activities. It is a state-of-the-art, open-source, multiplatform ontology engineering environment, which provides comprehensive support for the ontology engineering life cycle of networked ontologies.
The NeOn Toolkit is one of the major results of the NeOn project.
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