One of the primary distinguishing features of any conceptual model lies in its association with initial conceptual space, often referencing part or all of an ontology provided by the community of practice. Typically, this ontology is specified in OWL to foster reusability through the web. However, a definite mapping from OWL 2 to popular conceptual modelling languages like ORM, UML, or EER that helps in this process is missing. This work addresses this interoperability problem by proposing a tool-based framework to automatically generate conceptual model patterns through querying OWL reasoners. Intending to closely approximate the generated model to the original OWL 2 ontology, we identify five axiom patterns and their representations in ORM 2. Subsequently, we develop a procedure to import partial knowledge from ontologies by exhaustively searching the inferred axioms, normalising them, and translating them. We assess the feasibility of our approach by importing a set of publicly available ontologies and measuring the generated conceptual models using structural metrics. As a result of this experiment, we successfully imported around 40% of the LOV repository ontologies, with around 80% of the axioms recognised and translated.

In this work, we treat web interoperability in terms of interchanging ontologies (as knowledge models) within user-centred ontology engineering environments, involving visual and serialised representations of ontologies. To do this, we deal with the tool interoperability problem by re-using an enough expressive ontology-driven metamodel, named KF, proposed as a bridge for interchanging both knowledge models. We provide an extensible web framework, named crowd 2.0, unifying the standard conceptual data modelling languages for generating OWL 2 ontologies from semantic visualisations. Visual models are designed as UML, ER or ORM 2 diagrams, represented as KF instances, and finally, formalised as DL-based models. Reasoning results may be newly incorporated into the shared KF instance to be visualised in any of the provided languages.