In recent years, the process humans adopt to learn a foreign language has moved from the strict "Grammar –Translation" method, which is based mainly on grammar and syntax rules, to more innovative processes, resulting to the more modern "Communicative approach". As its name states, this approach focuses on the coherent communication with native speakers and the cultivation of oral skills, without taking into consideration, at least at the first stages, the rules that govern the language. The same trend seems to have been applied to the way machinery can be "educated" to comprehend and reproduce the unfamiliar, human language. The "rule based" Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms, on one hand, and the "text based" Large Language Models (LLMs), on the other, are two, analogous to the two human foreign language learning processes, subareas of Natural Language Processing (NLP). This paper presents these two alternative approaches, LLMs (a technology having surfaced as an influential catalyst of NLP, during last years) on the one hand and NLG/NLU on the other, highlighting their applications, their technologies, their capabilities, their differences, their strengths and weaknesses and the challenges they present, contributing to a deeper comprehension of the evolving landscape of Artificial Intelligence and human-computer communication.

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks.

Nowadays, the business area cannot be sustainable and efficient without the presence of the Supply Chain. However, Supply Chain Management is by no means an easy task. Experts, in their effort to achieve the most efficient Supply Chain Management, have turned their attention to the management of knowledge related to supply chains. Thus, they model the concepts and the semantic relationships between them in Supply Chain Networks and create conceptual models and ontologies. In this paper, a survey of the existing ontologies in this field is carried out, with the aim of creating a new ontology of the Supply Chain that will unify its structural elements and lead to the integration of all supply systems. For this purpose, 22 ontological models from different supply systems, from over 90 sources, have been collected, briefly presented and commented on. These models, although being an intersection in the effort to model business operations and delineate a good basis for businesses to engage in Logistics processes, they lack an adequate formulation of appropriate semantics and terminology to describe all the different functions of the Supply Chain. Consequently, this lack inevitably leads to incompatible interpretations and uses of the knowledge that results from intercompany transactions.

One of the issues in Natural Language Processing (NLP) and Artificial Intelligence (AI) is language representation and modeling, aiming to manage its structure and find solutions to linguistic issues. With the pursuit of the most efficient capture of knowledge about the Modern Greek language and, given the scientifically certified usability of the ontological structuring of data in the field of the semantic web and cognitive computing, a new ontology of the Modern Greek language at the level of structure and vocabulary is presented in this paper, using the Protégé platform. With the specific logical and structured form of knowledge representation to express, this research processes and exploits in an easy and useful way the distributed semantics of linguistic information.

In this paper, the aim is to present a project report of a method, based on which a small natural language lexicon is designed, or otherwise a small language model, for robotic applications. In this direction, a reference is made to Controlled Natural Languages, and their characteristics are determined for application in operational robotic vehicles. In addition, criteria are established for the selection of the nationality of the language, which constitutes the basis of the lexicon. A small introspection is made in the natural language unit of a human–robot interface, and a method is presented that examines the adequacy of the selected language in relation to the purpose of use of the system and the control criteria. The use of the lexicon’s method is presented, and an example of the analysis and inclusion of selected Greek language words is given. The reproduction of words from the lexicon’s stored data is also explained, as the reverse process that functions as a confirmation of the method. Received: 22 August 2025 | Revised: 20 April 2026 | Accepted: 12 October 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Ioannis Giachos: Conceptualization, Methodology, Software, Investigation, Resources, Writing – original draft, Visualization. Evangelos Papakitsos: Conceptualization, Methodology, Investigation, Resources, Writing – original draft. Nikoletta Samaridi: Investigation, Resources, Data curation. Michail Papoutsidakis: Validation, Formal analysis. Nikolaos Laskaris: Writing – review & editing, Supervision.

This study presents a comprehensive NLP infrastructure for Modern Greek that bridges grammatical analysis and ontological representation, integrating linguistic theory, algorithmic modeling, and semantic structuring within a unified computational framework. Modern Greek remains under-resourced in terms of interoperable linguistic tools, with existing systems addressing morphology, syntax, or semantics separately. The proposed system fills this gap by connecting data extraction, morphological generation, and syntactic analysis within a single executable environment, validated through diagnostic evaluation and ontology consistency checking. Linguistic data were automatically retrieved from the Dictionary of Standard Modern Greek (55,101 entries), processed through Python-based algorithms, and integrated into an ontology in Protégé. The resulting ontology captures grammatical and semantic relations with high precision and supports reasoning, interoperability, and linguistic knowledge expansion. The system transforms grammatical knowledge into ontological knowledge, providing a scalable linguistic foundation for future advancements in Natural Language Processing and Understanding for Modern Greek.