SeMFIS: A flexible engineering platform for semantic annotations of conceptual models

Article type: Research Article

Authors: Fill, Hans-Georg

Affiliations: Research Group Knowledge Engineering, University of Vienna, Waehringerstrasse 29, 1090 Vienna, Austria. E-mail: [email protected]

Abstract: In this paper, we present SeMFIS – a flexible engineering platform for semantic annotations of conceptual models. Conceptual models have been used in the past for many purposes in the context of information systems’ engineering. These purposes include for example the elicitation of requirements, the simulation of the behavior of future information systems, the generation of code or the interaction with information systems through models at runtime. Semantic annotations of conceptual models constitute a recently established approach for dynamically extending the semantic representation and semantic analysis scope of conceptual modeling languages. Thereby, elements in conceptual models are linked to concepts in ontologies via annotations. Thus, additional knowledge aspects can be represented without modifications of the modeling languages. These aspects can then be analyzed using queries, specifically designed algorithms or external tools and services. At its core, SeMFIS provides a set of meta models for visually representing ontologies and semantic annotations as models. In addition, the tool contains an analysis component, a web service interface, and an import/export component to query and exchange model information. SeMFIS has been implemented using the freely available ADOxx meta modeling platform. It can thus be directly added to the large variety of other modeling methods based on this platform or used as an additional service for other tools. We present the main features of SeMFIS and briefly discuss use cases where it has been applied. SeMFIS is freely available via OMiLAB at http://semfis-platform.org/.

Keywords: Conceptual models, semantic annotation, ontologies, analysis

DOI: 10.3233/SW-160235

Journal: Semantic Web, vol. 8, no. 5, pp. 747-763, 2017