Formal Ontologies in Information Systems Development: A Systematic Review Computational ontologies are machineprocessable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphsbased structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. Onehundred eightyseven studies were found during the first phase of the investigation. Twentyseven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

Enterprise Knowledge Modeling, UML vs Ontology: Formal Evaluation Everyday activities in enterprises rely heavily on the expertsxe2x80x99 knowhow. Due to experts departure, the loss of expertise and knowledge is a reoccurring problem in these enterprises. Recently, in order to capture experts knowledge into intelligent systems, formal knowledge representation methods, such as ontologies, are being studied and have caught up with nonformal or semiformal representation, such as UML. The similarities and differences between UML class diagram and computational ontology have for long raised questions about the possibility of synthesizing them in a common representation (usually an ontology). Indeed, the problem of migrating knowledge encoded in UML into an ontology is an active research domain. This paper outlines our approach, which is based on semantic matching between existing ontologies and a UML class diagram, to support UML driven ontology refactoring and engineering.