Why is ontology in designing software for an artificial manager important?

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Ontology is a word that comes from philosophy, but many scientific fields have borrowed ontology and established from it their first assumptions about the reality they study. The same is true for software to do anything in management, and more broadly in organizational reality. I will describe today, based on the literature, why ontology is so important.

An ontology is a formal, predetermined description of phenomena in a given slice of reality, whose characteristics are describable by certain variables or parameters [1]. However, many different definitions of ontology can be found in the literature. For example, ontology defines linguistic elements belonging to established concepts in order to construct knowledge [2]. It is also assumed that an ontology is an information system that contains the names of concepts in order to describe selected fragments of reality along with the adopted meaning assumptions [3]. W.V.O. Quinn [4] used to say that when it comes to ontology, millennia of ontological inquiry can be encapsulated in three words: “what is here?”. It must be admitted that this definition, although expressed by a question, is quite suggestive.

If you’re interested in why ontology is so important in our lives, see a great company about the importance of ontology in philosophy, which is really the science of what exists in the universe and the interpretation of that universe:

Ontology in the field of software design can be defined as “the set of activities that deal with the ontology development process, the ontology lifecycle, and the methodologies, tools and languages for building ontologies” [5]. Ontologies in software engineering offer a formal representation of knowledge. They are created to use a common vocabulary in a specific domain with the goal of sharing information through concepts and the relationships between these concepts [6]. The most important motivation for building ontologies in software engineering is to share a common understanding of information structure among application users and to enable them to reuse this knowledge.

I will now show research on the goals of using ontologies in software design, so in the case of the use of intelligent systems, which is to be the robot manager. The research showed that 72% of respondents expected the ontology to provide conceptual modeling and data integration. Slightly less, 65% of respondents said that the purpose of ontologies in software design is to define knowledge base schemas and link data from different public knowledge bases. Sharing knowledge and providing common access to heterogeneous data were indicated by 56% of respondents, and 50% indicated ontology-based search as a goal of software ontology [7].

Ontology in software design is a conceptual and terminological description of shared domain-specific knowledge, which means making improvements in communication using the same system in terms of terminology and concepts [8]. Ontologies are important parts of applications that support shared life, enabling analysis of high-performance datasets, data standardization and integration, search and discovery [9].

There is a view in the literature that regardless of the ontological assumptions made in a given scientific discipline (e.g., management science – author’s note), different objects of reality (organizational – author’s note) are understood differently by different researchers and within different research projects [10]. They can be objective, independent of the cognizing subject, or they can be subjective (in the original “values” – author’s note), forming an inseparable bond with the subject [11]. They can also be “quasi-objective” creations of the intellect, called conceptual objects and serving as instruments of cognition. Finally, they can be objects that are a mixture of all three approaches above.

In conclusion, I would like to emphasize that the ontology of reality, in our case organizational reality, provides a conceptual framework for representing, sharing and managing knowledge through a system of concepts, their hierarchy, the relationships assigned to them and the way they are semantically distinguished (El-Diraby, Lima, & Feis, 2005).

This is why ontological assumptions are so important in the concept of organizational size system, and nothing can be done without them if we want to build an artificial manager. Although I have already written several times about the ontological assumptions I built for this purpose, such as here: http://olafflak.home.pl/artificialmanagers/2023/02/20/what-is-the-world-of-an-artificial-manager-made-of-part-6-lets-combine-resources-and-processes/ I will devote several more posts to this topic in the future.

[1] Chang, X., Terpenny, J., & Koelling, P. (2010). Reducing Errors in the Development, Maintenance and Utilisation of Ontologies. International Journal of Computer Integrated Manufacturing, 23(4), 341-352.

[2] Hendler, J. (2001). Agents and the Semantic Web. IEEE Intelligent Systems, 16, 30-37.

[3] Corcho, O., Fernandez-Lopez, M., & Gomez-Perez, A. (2003). Methodologies Tools and Languages for Building Ontologies. Data and Knowledge Engineering, 46, 41-64.

[4] Brink, C., & Rewitzky, I. (2002). Three Dual Ontologies. Journal of Philosophical Logic, 31(6), 543-468.

[5] Cakula, S., & Salem, A.-B. M. (2013). E-learning developing using ontological engineering, WSEAS Transactions on. Information Science and Application, 10(1), 14-25.

[6] Gayathri, R., & Uma, V. (2018). Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey. ICT Express, 3(2), 69-74.

[7] Warren, P, Mulholland, P., Collins, T., & Motta, E. (2014). Using ontologies: understanding the user experience. In: Knowledge Engineering and Knowledge Management (pp. 579-590), Lecture Notes in Computer Science, Springer.

[8] Fonseca, V. S., Barcellos, M. P., & Falbo, R. (2017). An ontology-based approach for integrating tools supporting the software measurement process. Science of Computer Programming, 135, 20-44.

[9] Fraga, A. L, Vegetti, M., & Leone, H. P. (2020). Ontology-based solutions for interoperability among product lifecycle management systems: A systematic literature review. Journal of Industrial Information Integration, 20, 100176.

[10] Laudan, L. (1984). Science and Values: The Aims of Science and Their Role in Scientific Debate. Berkeley: University of California Press.

[11] Ghenea, S. V. (2015). On Facts and Values. Scientific Journal of Humanistic Studies, 7(12),  11-14.

[12] El-Diraby, T. A.,  Lima, C., & Feis, B.: Domain Taxonomy for Construction Concepts – Toward a Formal Ontology for Construction Knowledge. Journal of Computing in Civil Engineering, 19(4), 394-411.