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Proceeding Paper

Digital Semantics for Enterprise Information System Development †

by
Gaetanino Paolone
,
Francesco Pilotti
* and
Romolo Paesani
Gruppo SI S.c.a.r.l., 64100 Teramo, Italy
*
Author to whom correspondence should be addressed.
†
Presented at the 5th International Electronic Conference on Applied Sciences, 4–6 December 2024; https://sciforum.net/event/ASEC2024.
Eng. Proc. 2025, 87(1), 42; https://doi.org/10.3390/engproc2025087042
Published: 11 April 2025
(This article belongs to the Proceedings of The 5th International Electronic Conference on Applied Sciences)
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Abstract

This position paper discusses the use of Digital Semantics, ontologies, and automata in the era of Artificial Intelligence (AI). Digital Semantics represents a potential new definition and paradigm for simulating human intelligence within a machine. Integrating this paradigm with other AI research approaches can significantly enhance the future of AI and its relevance for Enterprise Information System (EIS) automation. Our proposal is based on three Research Questions (RQs). The ultimate goal of our research is to define a method that fosters the use of AI in EISs for business modeling, system modeling, design, and implementation.

1. Introduction

Artificial Intelligence (AI) is the most important paradigm shift of our time. In this regard, the goal is to simulate human intelligence within a machine, and AI is already impacting on many aspects of our life [1]. AI is “… the science and engineering of making intelligent machines, especially intelligent computer programs” [2] (p. 2). The core of AI research focuses on defining solutions to obtain, understand, store, and elaborate digital inputs in order to return results, i.e., solutions that emulate human thinking. Our contribution is a position paper about our vision and ideas on this topic. Our proposal integrates existing AI approaches, aiming to contribute to the research on AI knowledge and development (Figure 1).
AI is advancing towards the advent of increasingly powerful Machine Learning algorithms that can achieve high performance [3]. However, these algorithms are not comparable to the human mind, even though they can be designed and implemented in a systematic and interpretable way. Furthermore, companies seeking to implement AI still face significant challenges, primarily because understanding AI concepts remains one of the biggest obstacles [4]. AI is not inherently connected to fundamental mechanisms of human mind, such as abstraction, encapsulation, and reasoning—not just data storage and information retrieval, but also the application of algorithms for processing. For decades, researchers have attempted to define a logic-based language for conceptual modeling in the field of Information Systems Engineering. For example, in Ref. [5], a formal language was defined to provide an integrated representation of requirements from both a structural and behavioral perspective. This representation was achieved by defining the semantics of a terminological language, which was also used to capture concepts related to processing. With the advancement of Computer Science, AI has been applied to various disciplines, including natural language processing, robotics, semantic recognition, and information system development. In the context of natural language processing, AI-based human language recognition and comprehension have become key areas of research [6], where semantic processing plays a pivotal role. Similarly, several contributions have integrated AI components with conceptual modeling for the representation of Enterprise Information Systems (EISs) (e.g., [7]). Ref. [8] provides insights into the role of AI in the software development lifecycle. Ref. [9] presents a systematic literature review that offers a structured understanding of the state of the art in AI research within information systems, identifying 98 primary studies out of 1877 AI-related articles over a fifteen-year period (2005–2020). Additionally, efforts have been made to create and organize Knowledge Management Systems within the framework of information system development. These efforts include the analysis of different types of knowledge, the determination of the significance of implicit knowledge for cognitive activity, and the generation of new knowledge through the application of AI methods and models (e.g., [10]).
Our position paper fits within the aforementioned scenario. We propose Digital Semantics, a novel paradigm and definition to the best of our knowledge. We have defined a method to develop our proposal, limited to the EIS domain. The proposal is based on the use of automata and ontologies. Digital Semantics is the solution for defining ontologies, which, in turn, must be implemented by means of automata. It is worth noting that Digital Semantics is not related to the Semantic Web. The definition of a new, high-potential AI paradigm is based on three Research Questions (RQs):
  • (RQ1) Is it possible to define Digital Semantics as a metamodel based on the semantics of natural languages?
  • (RQ2) Is it possible to define ontologies using Digital Semantics?
  • (RQ3) Can automata provide a solution for implementing ontologies defined with Digital Semantics?
The goal of this position paper is to address these RQs and gather feedback from the international scientific community.
The remaining part of this paper is structured as follows. Section 2 presents the methods adopted in this position paper. Section 3 discusses our proposal. Section 4 concludes this paper and provides an overview of the long-term industrial research project.

2. Materials and Methods

The materials for formulating our proposal were primarily obtained from a search carried out on Scopus database on 29 September 2024, using the keywords reported in Table 1, which also presents the results. The search was restricted to conference proceedings, articles, and reviews, all written in English. Among the materials of our proposal, we must also consider linguistic universals in the context of natural languages. Linguistic universals, introduced by Greenberg [11], are properties common to all languages.
The results of the Scopus search highlight, on the one hand, the existence of proposals on the use of AI solutions in information systems development, and, on the other hand, a gap concerning the integrated use of AI, automata, and/or ontologies.
Our method is briefly illustrated below. The literature on AI, encompassing proposals, approaches, and models, is vast and spans numerous application domains. Our research goal is to propose a paradigm for simulating human intelligence within a computer, specifically within the EIS domain, using automata and ontologies. Today, automata are used in Software Engineering to manage decision-making processes, control information flow within software systems, and develop applications [12]. Semantics and ontologies are essential aspects of human intelligence. In Data Science, ontologies and Machine Learning are already employed to define the meaning of data and make them usable for computer processing [13]. The term “ontology” has multiple meanings depending on the discipline and domain [14,15,16]. In the EIS domain, we define “ontology” as a set of concepts and relationships that represent a specific knowledge area.
How can we create Digital Semantics? Our idea is to simulate the cognitive processes used by the human mind. The definition of Digital Semantics is rooted in the semantics of natural languages. In this regard, useful insights can be drawn from formal semantics [17], which is applied in the field of formal languages, forming the basis of all programming languages. Digital Semantics should provide models and meanings for concepts. The purpose of ontologies is to schematize the elaboration processes related to these concepts. Finally, we propose using automata to implement these processes. Figure 2 outlines the proposed method.
Our proposal is based on the method illustrated above, the aim of which is to address the RQs of this position paper. The answers are provided in the following Section.

3. Proposal

The aim of our proposal is to define a new paradigm within AI that integrates elements specifically related to Data Science in the EIS domain. The Data Science discipline primarily focuses on using Machine Learning algorithms and techniques to analyze data and return information. Machine Learning is increasingly utilized in the automation of EISs [18]. In particular, Machine Learning algorithms are employed in Decision Support Systems [19] and Knowledge Management Systems [20]. We propose to define, design, and implement automata capable of simulating and executing the normal reasoning mechanisms of the human mind, designed to be applied to a knowledge base. To substantiate the proposal, we present possible answers to the RQs of this position paper. The RQs are as follows:
  • (RQ1) Is it possible to define Digital Semantics as a metamodel based on the semantics of natural languages?
  • (RQ2) Is it possible to define ontologies using Digital Semantics?
  • (RQ3) Can automata provide a solution for implementing ontologies defined with Digital Semantics?
A subsection is provided to answer each RQ.

3.1. RQ1

Natural languages are infinite and constantly evolving. However, we believe that it is possible to abstract and formalize the processes and techniques underlying their semantics to define a metamodel. In this regard, we consider formal semantics an important reference for defining Digital Semantics. At a high level of abstraction, the process of defining a programming language mirrors the formalization of a natural language. From a syntactic perspective, this statement aligns perfectly with the evolution of Computer Science. All programming languages implement rigorous and formal syntactic rules, similar to natural languages. However, this is not entirely true when it comes to semantic rules. While modern compilers perform precise syntactic parsing, their capabilities for the semantic verification of algorithms remain limited. Complete control at the syntactic level enables automatic code generation, an area in which some of the authors have proposed approaches [21,22,23] to transforming Unified Modeling Language (UML) [24] diagrams into fully functional software applications.

3.2. RQ2

In Computer Science, it is already possible to create ontologies that represent any construct, whether an object or a process, within a computer. Ontologies are widely used in Software Engineering in, for example, Ontology-Driven Software Development (ODSD) [25]. In everyday life, our minds create and apply ontologies expressed through natural language semantics. Ontologies can represent basic concepts (e.g., “door”, “window”, and “roof”) or complex concepts that emerge from the combination of other ontologies, whether basic or complex (e.g., “house”). If ontologies can be defined in natural language, and if Digital Semantics is based on the constructs of natural language semantics, then ontologies can also be defined using Digital Semantics. The affirmative answer to RQ2 follows directly from the validity of RQ1.

3.3. RQ3

Ontologies defined using Digital Semantics must be represented within a computer. We propose using automata to create and apply ontologies in a computer’s memory. The automata to be employed are those well-established in Computer Science. Over the years, automaton models have been developed that effectively meet the need to implement both basic and complex ontologies. Specifically, we propose creating an automaton for each basic ontology (Figure 3).
A complex ontology can be defined as a combination of basic and/or other complex ontologies (Figure 4).
An automaton can implement a complex ontology. Consequently, automata of complex ontologies are composed of a combination of basic and/or other complex automata. (Figure 5).
Ontologies defined with Digital Semantics and implemented through automata can be used for EIS automation. In this context, ontologies are the subsystems of an enterprise system, while automata are the subsystems of an EIS.

4. Conclusions and Future Work

The position paper poses three RQs and proposes possible answers regarding the definition of a new AI paradigm for the design and implementation of EISs. This paradigm is based on the definition of Digital Semantics, the definition of ontologies, and the use of automata for ontology implementation. Our research aims to achieve an ambitious goal. To the best of our knowledge, no existing approaches or paradigms are similar to our proposal. Obtaining feedback from the international scientific community on our vision, ideas, and proposal is of paramount importance.
The proposal outlined in this position paper represents the early stage of a long-term industrial research project. We need to verify the soundness of our solution through further research and development activities. The next immediate step involves defining a Digital Semantics metamodel. Subsequently, it will be necessary to establish a method for representing ontologies based on Digital Semantics. Each future research step must be supported by concrete and applicable examples within the EIS domain.

Author Contributions

Conceptualization, G.P. and F.P.; methodology, G.P. and F.P.; investigation, G.P. and F.P.; data curation, R.P.; writing—original draft preparation, G.P. and F.P.; writing—review and editing, F.P. and R.P.; visualization, F.P.; supervision, G.P.; project administration, G.P.; funding acquisition, G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. The AI process.
Figure 1. The AI process.
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Figure 2. Method outline.
Figure 2. Method outline.
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Figure 3. Basic ontologies and automata.
Figure 3. Basic ontologies and automata.
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Figure 4. Complex ontologies: (a) a complex ontology combining two basic ontologies; (b) a complex ontology combining a basic ontology and a complex ontology.
Figure 4. Complex ontologies: (a) a complex ontology combining two basic ontologies; (b) a complex ontology combining a basic ontology and a complex ontology.
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Figure 5. Complex automata: (a) a complex automaton combining basic two basic automata; (b) a complex automaton combining a complex automaton and a basic automaton.
Figure 5. Complex automata: (a) a complex automaton combining basic two basic automata; (b) a complex automaton combining a complex automaton and a basic automaton.
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Table 1. Scopus search: keywords and results. The asterisk * is used at the end of some strings for truncation purposes (e.g., “Ontolog*” finds “ontology” and “ontologies”).
Table 1. Scopus search: keywords and results. The asterisk * is used at the end of some strings for truncation purposes (e.g., “Ontolog*” finds “ontology” and “ontologies”).
KeywordsDocuments
“Artificial Intelligence” AND “Information Systems Development”73
“Artificial Intelligence” AND “Information Systems Development” AND Automata0
“Artificial Intelligence” AND “Information Systems Development” AND Ontolog*7
“Artificial Intelligence” AND “Information Systems Development” AND “Tertiary stud*”0
“Artificial Intelligence” AND “Information Systems Development” AND (Review OR “mapping study” OR “Literature review”)6
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MDPI and ACS Style

Paolone, G.; Pilotti, F.; Paesani, R. Digital Semantics for Enterprise Information System Development. Eng. Proc. 2025, 87, 42. https://doi.org/10.3390/engproc2025087042

AMA Style

Paolone G, Pilotti F, Paesani R. Digital Semantics for Enterprise Information System Development. Engineering Proceedings. 2025; 87(1):42. https://doi.org/10.3390/engproc2025087042

Chicago/Turabian Style

Paolone, Gaetanino, Francesco Pilotti, and Romolo Paesani. 2025. "Digital Semantics for Enterprise Information System Development" Engineering Proceedings 87, no. 1: 42. https://doi.org/10.3390/engproc2025087042

APA Style

Paolone, G., Pilotti, F., & Paesani, R. (2025). Digital Semantics for Enterprise Information System Development. Engineering Proceedings, 87(1), 42. https://doi.org/10.3390/engproc2025087042

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