Formal Ontologies in Information Systems Development: A Systematic Review

Abstract

Computational ontologies are machine-processable 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 graphs-based 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. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven 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.

Tacit knowledge elicitation process for industry 4.0

“…The symbolic layer is where the concepts initially formed in the conceptual layer (and sometimes ill-formed) are validated to create a consistent operational KG. Here we mainly refer to ontology alignment [61,62] expressed for making integration into a data graph schema possible. The implicit knowledge is also converted into explicit knowledge and encoded in an ontological structure during the collaborative knowledge process (Sect.…”

Tacit Knowledge Elicitation Process for Industry 4.0

“…The symbolic layer is where the concepts initially formed in the conceptual layer (and sometimes ill-formed) are validated to create a consistent KG. Here we are mainly referring to ontology alignment [43,44] expressed for making possible the integration into a data graph schema. The implicit knowledge is also converted into explicit knowledge and encoded in an ontological structure during the collaborative knowledge process with experts (Section 4).…”

Modeling for Rapid Systems Prototyping: Hospital Situational Awareness System Design

“…SA systems are designed to communicate information in support of decision-making via representations [18,19]. The use of ontologies in designing the information embedded in such systems is essential to establishing explicit, sharable, reusable and interoperable knowledge representations [14,[20][21][22], and it enhances context-aware capabilities in product-service systems [17]. Research efforts have produced a multitude of healthcare related ontologies, such as an ontology for healthcare technology innovation [13], ontologies describing a ubiquitous computing environment for healthcare [23,24], ontology for healthcare networks [25] and breast cancer imaging ontology [26].…”

“…Information Technology (IT) systems are an enabler of modern healthcare [10,11]. In order to effectively communicate the information with various stakeholders, to produce insights with respect to the information and to support information-based collaborative work, the information should be well structured and clear, preferably standardized [12][13][14]. An information-driven SA system can be designed as a digital twin of a product-service system and may therefore provide the desired-but challenging to obtain-service context for healthcare product-service system development [15][16][17].…”