The aim of this critical review paper is threefold: (a) to provide an insight on the impact of ontology engineering methodologies (OEMs) to the evolution of living and reused ontologies, (b) to update the ontology engineering (OE) community on the status and trends in OEMs and of their use in practice and (c) to propose a set of recommendations for working ontologists to consider during the life cycle of living, evolved and reused ontologies. The work outlined in this critical review paper has been motivated by the need to address critical issues on keeping ontologies alive and evolving while these are shared in wide communities. It is argued that the engineering of ontologies must follow a well-defined methodology, addressing practical aspects that would allow (sometimes wide) communities of experts and ontologists to reach consensus on developments and keep the evolution of ontologies ‘in track’. In doing so, specific collaborative and iterative tool-supported tasks and phases within a complete and evaluated ontology life cycle are necessary. This way the engineered ontologies can be considered ‘ shared, commonly agreed and continuously evolved “live” conceptualizations ’ of domains of discourse. Today, in the era of Linked Data and Knowledge Graphs, it is more necessary than ever not to neglect to consider the recommendations that OEMs explicitly and implicitly introduce and their implications to the evolution of living ontologies. This paper reports on the status of OEMs, identifies trends and provides recommendations based on the findings of an analysis that concerns the impact of OEMs to the status of well-known, widely used and representative ontologies.

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming.

The integration of cutting-edge technologies—such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies. The areas examined in this study are open field smart farming, vertical and indoor farming, zero waste agriculture, precision livestock farming, smart greenhouses, and regenerative agriculture. This paper links current research, technological innovations, and case studies to present a comprehensive review of these emerging technologies being developed in the context of smart agriculture, for the benefit of farmers and consumers in general. By exploring practical applications and future perspectives, this work aims to provide valuable insights to address global food security challenges, minimize environmental impacts, and support sustainable development goals through the application of new technologies.

Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.