From Cogwheels to Concepts: A Perspective on the Interplay of Robotics and Ontology

Background. Modern trends, such as globalization, open markets, the development of knowledge-intensive industries, artificial intelligence, automation, and robotics, lead to a decrease in the need for labor since many tasks that humans previously performed can now be performed by machines. According to World Bank forecasts, about 600 million new jobs will be needed by 2030 to fill the growing global workforce. The international community recognizes that entrepreneurship can effectively perform the mentioned task, which makes its development and support a priority of the state policy of various countries. In developed countries the importance of creating entrepreneurial ecosystems as an environment and stimulus for the growth and support of entrepreneurship and innovation is recognized. However, there is no single definition of an entrepreneurial ecosystem, and the factors that contribute to an entrepreneurial ecosystem’s success are still being explored. Therefore, the purpose of the article is to summarize the existing theoretical approaches to the definition of the entrepreneurial ecosystem and its key elements. The object of research is the entrepreneurial ecosystem, which is considered a complex system consisting of various interconnected elements, including entrepreneurs, investors, mentors, educational institutions, and government bodies. Methods. General scientific and special research methods were used, including the system method, methods of analysis, synthesis, abstraction, and generalization. Results. The entrepreneurial ecosystem was defined as a set of legal entities and individuals from various sectors, different in nature of the activity, which functions for the development of innovation and entrepreneurial activity by combining the efforts of various groups of stakeholders. Key components of the entrepreneurial ecosystem include policy development, infrastructure, finance, innovation, markets, support, culture, and human resources. Entrepreneurial ecosystems can be developed through various activities such as creating an enabling environment for entrepreneurship, providing support to entrepreneurs, and raising awareness of entrepreneurship. Conclusions. The research demonstrated the high importance of entrepreneurial ecosystems for the development of business, described its major components, and identified the roles of the key stakeholders in the process of entrepreneurial ecosystem formation.

Performing a safe and successful cataract surgery greatly depends on the effective training programs of the residents that are commonly offered by several public and private healthcare providers. Virtual Reality Training (VRT) as a training tool for example, expresses potential capabilities to improve the learning curve, increasing trainee confidence and acquisition of skills. However, the success of this tool highly depends on how close this tool is to the reality. Ontology is called as a significant modelling tool which can help to provide a shared and common understanding of a domain, and in this scenario, a standardized terminology for representing the training domain, and actions taking place in Virtual Environment (VE). Recent Systematic Literature Review (SLR) findings show that most ontology designs for VRT are not built systematically and there is no ontology describing the domain knowledge for VRT in the ophthalmology field. There is also a lack of a high achievement rate in implementing ontology driven VRT, calls for systematic and comprehensive steps. Therefore, in order to lower the failure rate of ontology applied to VRT, OntoPhaco was designed and developed based on philosophically grounded foundational ontologies, namely, Unified Foundational Ontology (UFO) and Design & Engineering Methodology for Organizations (DEMO). The evaluation results showed that the use of OntoPhaco was able to improve the student’s learning experience, assist effectively in building VR training scenarios, and lead to a successful VRT processes. The lessons learnt from designing OntoPhaco for this domain could generally generate valuable contribution to the theory and practice of VRT, knowledge management and ontology engineering in complex domains.

The article is devoted to the problem of ontological engineering of a complex cyber-physical system. An example of such a system is a system that automates processes in higher education institutions. Ontological engineering is the process of designing and developing ontologies that combines two main technologies for designing complex cyber-physical systems - object-oriented and structural analysis. The goals of ontological engineering are to increase the level of integration of information necessary for making management decisions, increase the efficiency of information retrieval, and provide an opportunity for joint processing of knowledge based on a single semantic description of the knowledge space. Ontological engineering is carried out within the framework of the proposed approach to managing complex cyber-physical systems. The resulting ontology should be a convenient and flexible basis for modeling processes and ensuring the functioning of information and analytical processes in a complex system. Approaches to ontological engineering based on graphs are considered. The application of ordinary graphs, hypergraphs and metagraphs is described. The use of metagraphs in the construction of hierarchical ontologies has been substantiated. Metagraphs are considered as the basis for building an applied system ontology. A modification of the metagraph is proposed to include events and system methods in the ontology. This approach to the construction of a metagraph allows to include the process component in the ontological model of the system as an integral part of it, which allows to flexibly and with lower time expenses form process models based on segments of the general ontological model. An example of the implementation of a software-instrumental environment for ontological engineering and further construction of models of processes of a complex cyber-physical system is presented. The technology used to implement the ontological model in the database and the structure of the database for storing the ontology are described.

Ontology engineering plays a critical role in clinical decision support systems for Parkinson’s Disease (PD) monitoring and alerting. While Large Language Models (LLMs) have shown promise in knowledge modeling tasks, their effectiveness in autonomously constructing comprehensive ontologies for complex clinical domains remains unclear. This study investigates four ontology engineering methodologies for PD monitoring and alerting: One-shot (OS) prompting, Decomposed Sequential Prompting (DSP), X-HCOME, and SimX-HCOME+. Multiple LLMs were evaluated across these methodologies. Generated ontologies were assessed against a reference PD ontology using structural evaluation metrics focused on classes and object properties. Expert review was additionally conducted to analyze knowledge extensions beyond the gold standard. LLMs were able to autonomously generate syntactically valid and semantically meaningful ontologies using OS and DSP prompting; however, these ontologies exhibited limited conceptual coverage. Incorporating human expertise through X-HCOME significantly improved ontology completeness and evaluation metrics. Expert review further validated clinically relevant concepts absent from the reference ontology. SimX-HCOME+ demonstrated that iterative, supervised collaboration supports ontology refinement, although challenges persisted in natural language-to-rule formalization. The findings suggest that LLMs are more effective as collaborative assistants rather than standalone ontology engineers in the PD domain. Structured human–LLM collaboration is associated with improved ontology coverage and facilitates the identification of potential knowledge extensions in clinical monitoring applications. While the present evaluation focuses primarily on structural ontology elements, the proposed methodologies provide useful insights for LLM-assisted ontology engineering in complex healthcare domains.

Introduction. Generative design is a methodology based on the use of algorithms and applications to generate iterative and variable design solutions. It allows analyzing and considering multiple factors such as topography, climatic conditions, human flows, transportation networks and other parameters to develop the best solutions for a specific location. Ontology engineering is concerned with the creation of formal descriptions and models to represent knowledge about the subject area. In urban planning, ontology engineering can be used to create a formal model of a city that integrates information about its physical environment, infrastructure, public spaces and transportation network. Materials and methods. The realization of the project is based with on machine learning, convolutional neural network in the field of generative design. We describe the process of developing a system for determining and visualizing the optimal location of construction objects in urban planning, using park areas as an example. Results. The ontological model of CP 475.1325800.2020 “Parks. Rules of urban planning and landscaping”. A method for creating a park area layout using generative design is proposed. An example of the implementation of the proposed method using the Unity cross-platform computer application development environment is given. Conclusions. By combining generative design and ontology engineering in urban planning, new opportunities arise for designing innovative urban environments. With generative algorithms, ontology models can be used to automatically design and evaluate different urban design options, taking into account the given parameters and goals. This allows to explore a large number of options and find optimal solutions considering multiple factors. The paper analyzes the use of generative design and ontology engineering technologies in the field of urban planning.

The article deals with the issues of ontological engineering of complex systems. Ontological engineering includes the processes of designing and building ontologies, technologically combining object-oriented and structural analysis. Ontological engineering aims to ensure the adoption of high-quality management decisions by increasing the level of integration of the necessary information, improving search capabilities in databases and knowledge bases, providing the possibility of joint processing of knowledge based on a single semantic description of the knowledge space. This process is carried out within the framework of the proposed approach to managing complex systems. The ontology obtained as a result of engineering is subject to the requirements of convenience and flexibility, which is necessary for modeling system processes and ensuring the functioning of information and analytical processes in a complex system. The application of ordinary graphs, hypergraphs and metagraphs in ontological engineering is described. The use of metagraphs in the construction of hierarchical ontologies is substantiated. Metagraphs are considered as the basis for building an applied ontology of a complex system. A modification of the metagraph is proposed, which makes it possible to include events and data processing methods in the ontology. Such a modification integrates the process component into the ontological model of the system as an integral part of it, which makes it possible to flexibly and with less time to form process models based on the metagraph subgraphs of the general ontological model. An approach and an example of the implementation of the software-instrumental environment of ontological engineering and further construction of models of processes of a complex system are described. The technology used to implement the ontology in the PostgreSQL database management system and the database structure for storing the ontology are described

In this paper, we first introduce the notion of gloss for ontology engineering purposes. We propose that each vocabulary in an ontology should have a gloss. A gloss basically is an informal description of the meaning of a vocabulary that is supposed to render factual and critical knowledge to understanding a concept, but that is unreasonable or very difficult to formalize and/or articulate formally. We present a set of guidelines on what should and should not be provided in a gloss. Second, we propose to incorporate linguistic resources in the ontology engineering process. We clarify the importance of using lexical resources as a "consensus reference" in ontology engineering, and so enabling the adoption of the glosses found in these resources. A linguistic resource (i.e. its list of terms and their definitions) shall be seen as a shared vocabulary space for ontologies. We present an ontology engineering software tool (called DogmaModeler), and illustrate its support of reusing of WordNet's terms and glosses in ontology modeling.

Sundown of the twentieth century saw the emergence of the World Wide Web. A decade later, semantic web (SW) envisioned enriching of web-accessible information and services with machine-processable semantics to solve the problems of information management and sharing which are aroused by the success of the web. However, success of the SW largely depends on the availability of formal ontologies for representing and structuring information in different domains. To facilitate web ontology engineering (OE), methodologies are proposed by the researchers since years. However, OE particularly web OE is still immature and none of the methodologies is standardized for web OE projects as yet. This paper presents an evolutionary SW ontology engineering methodology called POEM. POEM presents a complete methodology comprising of various phases for performing conceptualization, designing, implementation, evaluation, and project management tasks. POEM exploits and incorporates the large experiences from the widely used software engineering standards to make the methodology more realistic and provide greater functionality. Due to its broad nature, POEM has the potential applicability in a wider range of OE projects. Comparison of POEM with other OE methodologies has ascertained POEM as more advantageous and easy methodology for SW ontology engineering.

In order to utilize methodology for ontology building more effectively,the Track Methodology Model(TMM) was created.The elements in TMM were defined and described,which included ontology(Top-level Ontology,Domain Ontology and Application Ontology) and five stages in the process of ontology building(analysis,design,development,deployment and evaluation).The qualitative analysis was explored based on the elements.Hence,three methodologies(driving wheel methodology for top-level,domain and application ontology) were imported,while their characteristics and relationships of three methodologies were researched and established.Through the application of building knowledge ontology in university based on TMM,effective methodology for building ontology can be selected to instruct the process of ontology building.

This paper presents the design and development of a novel biomimetic quadruped robot for behavior studies of rats and mice. Many studies have been performed using these animals for the purpose of understanding human mind in psychology, pharmacology and brain science. In these fields, several experiments on social interactions have been performed using rats as basic studies of mental disorders or social learning. However, some researchers mention that the experiments on social interactions using animals are poorly-reproducible. Therefore, we consider that reproducibility of these experiments can be improved by using a robotic agent that interacts with an animal subject. Thus, we developed a small quadruped robot WR-2 (Waseda Rat No. 2) that behaves like a real rat. Proportion and DOF arrangement of WR-2 are designed based on those of a mature rat. This robot has four 3-DOF legs, a 2-DOF waist and a 1-DOF neck. A microcontroller and a wireless communication module are implemented on it. A battery is also implemented. Thus, it can walk, rear by limbs and groom its body.

Research is underway at the NASA Glenn Researc h Center to develop and demonstrate the core technologies required to enable new and revolutionary approaches to engine diagnostics. One such effort investigates the use of multi -agent robots for autonomous search and inspection of propulsion systems. In t his envisioned application, on -wing engine inspections will be performed by groups of miniature mobile inspection devices that will traverse the interior surfaces of engine components in a coordinated, comprehensive search and inspection for damage. The cu rrent effort consists of several parallel activities that include: investigation into a range of algorithms for cooperative search, coverage completeness and obstacle avoidance; development of 3 -D graphical simulation software that will serve as a virtual test -bed to facilitate the testing and validation of the control algorithms; and development of demonstration robots that will allow the integration of control algorithms onto hardware. These activities will culminate in a proof -of -concept demonstration in which inspection robots will work together in a coordinated fashion to search for damage targets in a hardware test -bed environment. This demonstration will validate the feasibility of using multi -agent robotics to perform cooperative inspections in app lications such as on -wing in situ turbine engine maintenance .

Purpose. Methodological differences in the verification standards for the results of scientific knowledge between individual psychological branches represented in the domestic and global scientific space, the peculiarities of extremely complex psychological knowledge objects indicates the need to modernize the relevant standards and choose the optimal methodological platform for the verification of the results of modern psychological research. This paper discusses the feasibility of choosing such a ontological modeling platform, which is a special descriptive methodology and an alternative to the naturalistic experimental approach in verifying the results of psychological research. Ontological modeling provides the possibility for a more objective verification of the achievements for the study of intelligible objects, which have a distinct cultural and historical meaning, extremely complex structure and functionality, an ancient and diverse history of their own empirical representations. The purpose of the article is a theoretical study of ways to modernize the standards of verification of the results of modern psychological research using the example of the verification of the promising theory of game modeling of human vital activity in uncertainty conditions. Methods.The research uses the methods of analysis and systematization for psychological and interdisciplinary approaches in the study of verification patterns for the psychological knowledge results, comparison and optimization for the content of its standards on the study example of game modeling of human vital activity in uncertainty conditions. Results. According to the results of the study, the expediency of choosing ontological modeling (the names "conceptual modeling", "formal ontology", "formal ontology" and "ontological engineering" are also used synonymously) as a perspective basis for the verification of the psychological research results, a new universal methodological tool of Ukrainian psychology, has been proven using the example for game modeling of human vital activity in uncertainty. The existing scientific approaches that argue for the choice of ontological modeling as the basis for the verification of new psychological knowledge are specified. The methodological perspective of the modernization for the verification norms of psychological theories and concepts based on the ontological approach has been updated and substantiated. Modern scientific ideas are generalized and fundamental approaches to the verification of the results of knowledge of unlimited generality objects, i.e. ultra-complex simultaneously intelligible and empirical objects, are deepened. It has been established that ontological modeling has a universal scientific purpose and is able to overcome the shortcomings of the naturalistic experimental method for verification of research products, to be the optimal methodological platform for the verification of the modern psychological research results. Conclusions. The results of the study allow us to conclude about the relevance of the above issues and the expediency of choosing ontological modeling as a prospective basis for the verification of the psychological research results using the example for game modeling of human vital activity in uncertainty. A well-founded description of the standards and the process for verification of the psychological research results for game modeling of human vital activity in uncertainty conditions is considered promising.

This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its nervous system. The main objectives are to shed new light into the role of neuronal synchronisation and phase towards the generation of cognitive behaviours and to initiate an investigation on the efficacy of such systems as practical robot controllers. The first experiment is an active categorical perception task in which the robot has to discriminate between moving circles and squares. In the second task, the robotic agent has to approach moving circles with both normal and inverted vision thus adapting to both scenarios. These tasks were chosen for being considered as benchmarks in the evolutionary robotics and adaptive behaviour communities. The results obtained indicate the feasibility of the framework in the analysis and generation of embodied cognitive behaviours.

Grounding language in action and perception: From cognitive agents to humanoid robots

In recent years, many technical disciplines systematically develop standardized ontology that domain experts agree upon and apply to share and annotate information in their fields. The formal ontological representation facilitates R&D knowledge sharing and re-use by both knowledge workers and application systems. In this research, a system engineering approach for creating and managing domain ontology (called ontology engineering, OE) is proposed. The proposed methodology describes an integrated approach in five steps. The approach combining with XML representation schema is adopted to develop a computer-aided ontology engineering (CAOE) tool for effective engineering knowledge construction. The application of the methodology and OE tool application for R&D knowledge management is then investigated through a case study. The case study depicts the ontology engineering for R&D of the monetary bill inspection machine. The machine design knowledge ontology is applied to the related patent search analysis and synthesis to support knowledge management during the machine design and development.KeywordsOntologyontological methodologyknowledge managementbill inspection