The methodological foundations for the formation and support of transdisciplinary research using the methods and tools of ontological engineering have been developed. The stages of formation of the theory of transdisciplinarity are defined, in which the processes of building the categorical level of concepts and integration of knowledge domain, the formation of clusters of convergence of subject disciplines, the scientific picture of the world and the corresponding global network of transdisciplinary knowledge take a special place. In this case, a special role belongs to informatics as a system-forming branch of knowledge. The development of the NBIC-cluster of convergence opens wide, not yet sufficiently assessed, possibilities of a global knowledge-oriented Internet, but with it also the whole of modern civilization. Obviously, this development will follow first the path of creation of the applied dis-tributed systems in specific knowledge domain (Internet of Things, Smart systems in telemedicine, environmental monitoring, information support of goods and services, energy systems, utilities, etc.). Grid-, Blockchain-technologies and Cloud-computing, as well as virtual organizations, structures and services will occupy the central place in them.
In the modern scientific picture of the world (SPW), based on the ideas of the general concept of
evolution, self-organization, co-evolution, nonlinearity, it is assumed that the subject understood as
a society, enters the system it cognizes as an active component of this process-system [
A well-known philosophical concept called the transhumanism, which is exploring the
possibilities and consequences of achievements of science and technology, the dangers and the
benefits of using them. In opposition to transhumanism, the ideas of posthumanism are represented,
the central thesis of which is the acceptance of Man, Society and Nature as three jointly evolving
systems [
Techno-science have become a force capable of fundamentally changing the nature of man and
his life activity. At one time it was necessary to expand the scientific worldview what requires
science more deeply and intensively penetrating into the essence of the laws of nature and society,
than it was possible to do with the help of disciplinary and interdisciplinary approaches. J. Piaget
believed that “after the stage of interdisciplinary research, one should expect a higher stage —
transdisciplinary, which will not be limited to interdisciplinary relations, but will place these
relations within the global system, without strict boundaries among disciplines. Transdisciplinarity
should be considered as a new field of knowledge, different from
multidisciplinarity and
interdisciplinarity” [
In this paper, these terms are understood depending on the “distribution” of concepts and scientific disciplines with respect to the ontological levels of hierarchy, which implies different patterns of their interaction. Such distribution is essential when considering the methodology of the TD interaction, system integration of knowledge of subject disciplines and formation of the “convergence clusters” in implementation of transdisciplinary projects and their information and technology support.
Such notions as Noosphere, Object, Process, System, Information, Nature, Society, Man, Knowledge Domain, Science, Scientific Activity, Scientific Picture of the World, Engineering, Technology, etc. refer to the category level. Such concepts as Philosophy, Physics, Mathematics, Biology, Chemistry, Medicine, Humanities and Social Sciences, Informatics, Nanotechnologies, etc. refer to the level of domains of scientific disciplines. Many scientific disciplines which are directions, sections and sub-domains of domains, refer to the level of scientific disciplines.
The scheme of the categorical level of concepts is shown in Figure 1, and the concepts of the levels of domains and disciplines – in Figure 4.
Disciplinarity allows science to develop progressively within subject directions, and a disciplinary
approach divides the surrounding world into separate subject areas. If solving a task or problem goes
beyond the scope of disciplinary approaches, it is commonly believed that it is “at the intersection of
scientific disciplines”. In the process of progressive development of the disciplinary approach, an
opposition occurs, which conditions on the one hand, the accumulation of disciplinary knowledge,
and on the other, is the establishment of a natural limit to the fullness of knowledge of the
surrounding world. The way out of this situation is indicated by the following thesis: “If it is
impossible to go beyond the disciplinary direction, then the scope of disciplinary methodology can
be extended” [
Interdisciplinarity involves the integration of several scientific disciplines. One of them plays a leading role, and the results of ID-research are always interpreted in terms of leading discipline. The peculiarity of the ID-approach is that it allows direct transfer of research methods from one scientific discipline to another. The transfer of methods in this case is due to the discovery of similarities of the studied subject areas. ID approach is intended first of all, to solve specific disciplinary problems in which solving in any particular discipline the conceptual and methodological difficulties arise.
Synergetic paradigm as a section of the ID-approach in the hierarchy of knowledge occupies a
special place. On the one hand, it appeals to integrity and integral representation, systematically
determining the effects of objects, processes and subjects’ interaction, on the other hand, focuses on
nonlinearities, instabilities and the appearance of attractors, which ultimately change multilevel
organization and system behavior. In both cases, it is expressed by a set of formal models of
selforganization and is directed to reproduce the scientific picture of the world, which is especially
important during the transition to a transdisciplinary approach to research and implementation of
the paradigm of global evolutionism. SPW can be represented as a TDontology, which incorporates
not only the ontologies of individual disciplines, but also the methods of the latter, including variants
of their cross exposure. TD allows one to build a unified TD-methodology of analysis and synthesis,
including it in the general scientific picture of the world. In more detail the problems of synergy are
considered in [
In a multidisciplinary approach, researchers form a generalized picture of the subject of the research, in relation to which all of its disciplinary pictures appear as its equal in rights parts. The accumulation of results of multidisciplinary research in similar areas of disciplinary knowledge leads to the emergence of new multidisciplinary disciplines, such as physic-chemical biology, ecology, etc. The multidisciplinary approach has found its practical application, in particular, in the work of expert groups.
Multidisciplinarity does not imply the transfer of research methods from one discipline to another. All disciplines retain their subject directions.
TD system approach uses the knowledge generated and accumulated by disciplinary, interdisciplinary and multidisciplinary approaches. TD should ensure coordination and integration of disciplinary knowledge on the basis of a single axiomatic approach (the TD general systems).
This is how TD was originally imagined by J. Piaget and E. Jantsch [
Transdisciplinarity is a research strategy that crosses disciplinary boundaries and develops holistic (priority consideration of the whole in relation to its parts) vision. TD in the narrow sense means integration of various forms and methods of research, including special techniques of scientific knowledge, for solving scientific problems. TD to wide extent means the unity of knowledge beyond specific disciplines.
Let us note the changes in the structure of science, due to the transformation of a disciplinary
organized science in TD research: highlighting the following signs of the post-nonclassical stage: a
change in the nature of scientific activity, due to a revolution in the means of obtaining and storage
of knowledge (computerization of science, fusion of science with industrial production, etc.); increase
of the importance of economic and socio-political factors and goals; change of the object of study
itself — open self-developing systems (“human-sized” objects, examples of which are biotechnology
objects, ecological systems, biosphere, etc.) [
TD-studies, capturing the border zones (demarcation) areas of scientific disciplines, integrate the
essential foundations of the latter, forming the so-called convergence clusters, in which a powerful
synergistic interaction occurs due to the interpenetration of paradigms and specific current results
of each of the disciplines included in one or another cluster. This interaction reflects the integrity of
the real world, being an incentive and at the same time a guarantee of the success of TD research
and related practical projects, the non-triviality and significance of their results [
Currently, there is a tendency to intensify scientific research both at the intersection of different
subject disciplines (interdisciplinary research) and in convergence clusters (transdisciplinary
research). To support these studies, important factors are the construction of knowledge-oriented
information systems, improvement of research organization processes, improvement of methods and
tools for ontological analysis of natural language objects using generative language models to extract
knowledge from them, applied aspects of using ontologies, meta-ontologies, knowledge integration
systems in transdisciplinary convergence clusters [
The rapid development of convergent technologies has the potential to enhance both human activity and national economies. Examples of gains include increased work and learning efficiency, enhanced individual sensory and cognitive capabilities, fundamentally new manufacturing processes and improved products, revolutionary changes in healthcare, improved individual and group effectiveness, highly effective communication methods, including brain-to-brain interaction, improved human-machine interfaces, including neuromorphic engineering for industrial and personal use, enhanced human capabilities for defense purposes, achieving sustainable development with the help of NBIC tools, as well as improving the physical and cognitive decline characteristic of aging people.
The cluster of NBIC-convergence can serve as a vivid example of technoscience. Informatics brings to this cluster both system-forming, and computer-technological components. The main breakthrough directions in these clusters are: erasing the faces between living and inanimate systems, nanorobotics with its numerous applications, global supercomputer agglomerations with a high level of artificial intelligence. To these should be added the unified distributed TD-knowledge system as a globally-communicative version of a general scientific picture of the world and the next stage of development of the existing Internet and the Semantic Web.
A possible scheme of forecasting and targeted formation of promising convergence clusters, their synergistic interaction and obtaining aggregate efficiency for humanity requires separate publications. In Figure 2 shows, for example, a diagram of the connection between cognitive technologies and various fields of science and practice.
Other sectors
Education ECONOMICS
Health, Medicine Cognitive technologies (KOGNO )
COGNITIVE
NEUROSCIENCES INFORMATION TECHNOLOGIES AND TELECOMMUNICATIONS
Mathematics and cognitive neuroscience
Systems neuroscience Science and cognitive study
Multimodal interaction
NEUROINFORMATICS Logic, language and computerization
Causality and decision-making Artificial intelligence
Bioinformatics Cognitive and computational
psychophysics Biobots
Motion prostheses
Communication Brain↔ machine
Transportation
driving
Neuroengineering
Thus, TD research is a qualitatively new stage in the integration of science and society. For its completion, the scientific community still has much to design and develop, in particular: • • • • •
General scientific picture of the world, including subject disciplines, and the corresponding global network of TD knowledge.
Metatheory and metalanguage of TD.
The systemology of TD interaction, the figurative conceptual apparatus and models, the possibilities of which would allow, on the one hand, to cover all factors forming a complex problem and affecting it, on the other hand, to identify and take into account the mechanisms by which this effect is carried out.
The method (or a set of methods) of system research, providing access to all disciplinary information and its analysis, is understandable and available to specialists of any scientific discipline.
Prospective and self-sufficient convergence clusters that make up the core of the sixth technological order.
The methods of conducting experiments that allow studying the multifactorial effects of
objects of knowledge and evaluating their results [
The ways of posing and solving complex multi-factor problems in science, engineering and technology.
4. Transdisciplinarity—Noosphere Concept—Picture of the World It is in the transition to a knowledge society and TD-knowledge-oriented technologies the system forming role of informatics is manifested for real. The path to the TD lies through the creation of the system of ID interactions (in the light of the evolution of scientific theories) as an independent branch of knowledge. Moreover, informatics, in addition to a clear mathematical basis, also grasps technologies of posing and solving complex scientific-engineering problems.
Thus, the essence of the TD approach to the study of complex scientific and technical problems consists in effectively ensuring the dual unity of the concepts of deepening specific knowledge in the subject area, on the one hand, and expanding the coverage of the problem, based on the reality of the unity of the world, and recreate a holistic SPW on the other.
The terms: noospherogenesis, transdisciplinarity, ontology-driven systems, virtual paradigm,
information and cognitive support for scientific research, personalized knowledge bases, smart
projects, Internet things clearly outline, first of all, the subject area of informatics and information
technologies of the XXI century, focused directly on the stage of human development, based on
knowledge economy. In fact, the process of noospherogenesis, according to V.I. Vernadsky, touches
on the deeper aspects of interaction in the “Man-Nature” system. It appeals to scientific thought and,
consequently, to the cognitive resources of the human mind and SPW, the construction of which is
impossible without the TD approach to science and human civilization as a whole [
Figure 3 shows a variant of the categorical level of the noospheric paradigm and possible options for the development of human society. The main concepts are dominated by the triad of “knowledgeoriented concept—collective mind—sustainable development of society”. Currently present in the figure the concept of “Self-Destruction”, for obvious reasons, is becoming very relevant. It should constantly remind Humanity of the possible consequences.
TD-studies allow one to understand the complexity of the problems being solved, to take into account the diversity of ideas about the life world and the problems posed, to link abstract and concrete knowledge with the use of the “ontological engineering” and global network of TD knowledge.
Here is an example of a numbered list: 1. 2. 3. 4.
Analysis of the problem, its identification and structuring.
The development of the missing theoretical material necessary for problem solving. The construction of clusters of convergence of subject areas.
Choice of a language (or development of the new one) for the formalized presentation of knowledge domain and corresponding computer ontologies.
Practical implementation of the project.
5. Basis of Ontology-oriented Support for TD Research The research methodology and design of the mechanism of ID interaction in solving complex scientific and technical problems are associated with the creation of a conceptual framework of scientific theories. A set of 〈, , 〉 formal computer ontologies of specific knowledge domain may be such a framework. Formally, such an ontology can be represented by tuple [18, 19, 20]: = 〈, , , 〉, where , , , — finite sets respectively: — concepts of knowledge domain, — relationship between them, — interpretation functions (both and ), – axioms [20].
The implementation of the ontological concept involves a complete description of knowledge domain, for this it is necessary: (1)
To form and process (perform semantic analysis) the integrated linguistic corpus of texts by given subject areas.
To build ontographs (sets , ) for each knowledge domain.
To formalize descriptions of knowledge in the form of a scientific theory.
To carry out procedures of processing and integrating subject knowledge using semantic
analysis systems of source text materials and analytical processing and presentation. An
example is the toolkit Polyhedron (transdisciplinary ontological dialogues of object-oriented
systems), which is presented as a complex of program-informational and methodical
knowledge management tools using approaches of ontological management of corporate
information resources [
The role of ontologies of subject knowledge, besides the traditional functions of conceptualization and specification of scientific theories, is in the implementation of ontological management at the level of computer system architecture.
In more detail the categorial and domain levels of knowledge in the scientific and ontological picture of the world are presented in Fig. 4. It is clear that all aspects (even basic ones) of the indicated levels of knowledge cannot be reflected in one scheme. Therefore, here the emphasis is on the category “Nature → Society → Man → Knowledge of the World → TD scientific research”.
The Figure 4 shows three generalized levels. 1. Properly, the actual level of categories, structured into six sublevels (0 ÷ 5) in accordance with the categorial relationship. 2. The level of domains of subject and scientific disciplines. It is divided into three sublevels (1 ÷ 3), which mainly reflects the domains of the branch of science. Starting from this level, the semantic relations between concepts are already amenable to some scientific understanding. Most of them are represented by “be whole”, “be part”, “be species” and “be genus”. 3. The level of disciplines is represented only by the root vertices of the subject disciplines and technologies of the sixth technological structure. At this level, “whole-part” relationships prevail. Let us briefly describe the levels listed above.
Categorial level. A rule for constructing any ontograph is to indicate the root vertex concept,
which includes in its scope all the underlying vertices-concepts. In this case, such a vertex is the
category “Universe” (B) (level 0). Formally, B is a type of the highest (zero) level of categorization,
has no differentiation [
Level 1 contains the categories “Cosmos”, “Noosphere”, “Object” and “Process”. The category “Cosmos” on the presented ontograph (see Figure 4) is not considered in detail (at this stage). The category “Noosphere” is represented by the ontograph. The categories “Object” and “Process” are included to separate concepts into static and dynamic types. The ontograph does not contain the categories “Material” and “Abstract”, because the thematic focus of the ontograph involves the inclusion of mainly abstract concepts.
The categories of sublevels 2 ÷ 4 reflect the essential basis of TD research, and the categories of sublevel 5 detail them. The level of domains plays an important role in the formation of “convergence clusters” organically connected set of scientific theories, modem technologies and technical branch achievements. The indicated coherence is clearly manifested in the above-mentioned NBICtechnology cluster.
The level of disciplines specifies the subject disciplines, scientific theories and technologies. The
scheme of categorial level of concepts is structured in accordance with ontological principles, the
logical law of the inverse relationship between the volume and content of concepts and
claimsstatements that are recommendatory in nature [
Development and application of intelligent information systems (IIS) in various areas of human activity led to the creation of IIS of a new class, combining the properties of TD, ontological management, united by the concepts of purposeful development and virtuality. This is TD class of developing ontology-driven systems of research design. In addition to the tasks of infrastructural support for scientific research, here the tasks of their methodological support and ensuring the processes of integration, convergence, unified representation of TD knowledge and operations on them come to the fore. A significant role is played by systemological skills training and expansion of range of worldviews of scientific researchers to ensure the dual unity of the concepts of deepening knowledge in a particular subject area, on the one hand, and expanding the scope of the problem, based on the reality of the unity of the world and need to form a unified system of knowledge about the world, on the other hand [22].
As stated above, the fields of application of TD studies are constantly expanding, which, in turn, requires the continuous improvement of information (including supercomputer) technologies to support them. At the same time, the social component is added to the requirements, for which the parameters of reliability, efficiency and safety prevail. When considering the process of natural development of science and increasing demands on it by society, an integrated information technology system, which provide organizational processes, monitoring of scientific research, regulates all stages of their life cycle and electronic document management, analysis and evaluation of research results, decision making and determination current trends, etc., should be a basis for managing TD research. As a result, a common integrated space TD-knowledge is created, where synergistically many teams of professionals from different subject areas can interact, which will focus on solving the most important TD scientific and practical problems [23].
The core of integrated information technologies for TD scientific research consists of systematically integrated bases of structurally presented knowledge, distributed knowledge-oriented services, providing highly organized access to information and computing resources, the performance of such functions: identification of patterns, support of decisions making, cooperative collaboration virtualization, outsourcing, application of modern methods of processing multimedia information resources in virtual hyperspace.
Transition from the nondeterministic mode of production and use of knowledge by subjects of the scientific process to the mode of effective knowledge management, presented in a unified form at all stages of their life cycle, will ensure the growth of the effectiveness and quality of scientific research. At the same time, sustainable knowledge will become an intellectual capital, and the subjects of science will be direct participants in the economic activities of the society, which will create favorable conditions for stimulating the development of both science and creative society [23 – 26].
TD in turn puts forward the requirement of integration of scientific disciplines on the basis of formalism, common to all subject areas. This is the formal computer ontology. Hence, the process of noospherogenesis is based on the paradigmatic tuple (noosphere-SPW-transdisciplinarityontological concept-applied intellectual systems and technologies).
So, let us formulate an open (complemented and developing) system of requirements to information technologies supporting TD research.
1. New computer technologies should be built on the basis of knowledge adequate to the processes of solving problems in science, nature and society. 2. Continuous improvement of both the technologies themselves and the methods of manipulating them.
Promising modem methods of processing data, information and knowledge should be supported, including: close interaction with Grid-, Cloud- and Supercomputer technologies; ability to handle large amounts of data (Big Data); multi-factor authentication; focus on Green Computing (environmental technologies).
V.I. Vernadsky teaching about the noosphere in its essence appeals to the SPW, which must be built in order to overcome ID barriers and increase effectiveness of interdisciplinary interaction and modern science in general. It is about creating universal TD knowledge.
The development of the NBIC-cluster of convergence opens wide, so far completely not assessed, possibilities of a global knowledge-oriented Internet, but with it also the whole of modern civilization. Obviously, this development will follow first the path of creation of the applied distributed systems in specific subject areas (Internet of things, smart systems in telemedicine, environmental monitoring, information support of goods and services, energy systems, utilities, etc.). Grid-, Block-chain-technologies and Cloud-computing, as well as virtual organizations, structures and services will occupy the central place in them.
Thus, the problems of effective support for scientific TD research will lead to the formation and systems analysis of the service-oriented paradigm of noospherogenesis, transdisciplinary approach, ontological concept, the SPW, taking into account promising and informational technologies. The essential function of this paradigm is fully defined and, in fact, makes the methodological basis of modern scientific research as the basis for the development of civilization. Noospherology, as noted above, is a complete body of knowledge that ensures harmonic interaction in the “Man-Nature” system under the control of scientific thought and the will of man. It is organically linked with the scientific and technological components of the development of civilization. The development of science has moved from the stage of differentiation to the stage of integration, making it possible to implement the TD concept of the development of science, which appeals to SPW in the formulation and conducting of research and implementation of complex research projects. Without it, a purposeful positive process of noospherogenesis is unthinkable. Here informatics fulfills its mission as a backbone branch of knowledge. An ontological concept has arisen in its depths, the essence of which consists in the formal ontological description of subject regions and SPW as a whole. Finally, modem information technologies have already become the basis of almost all Hi-Tech and construction of knowledge-oriented society, capable of resolving all essential contradictions of the development of modem (technological) civilization. [15] A. Quamar, C. Lei, D. Miller, F. Ozcan, J. Kreulen, R. J. Moore, V. Efthymiou, An Ontology-Based Conversation System for Knowledge Bases, in: SIGMOD/PODS '20: International Conference on Management of Data, ACM, New York, NY, USA, 2020. doi:10.1145/3318464.3386139. [16] O. V. Palagin, V. V. Kaverinskiy, K. S. Malakhov, M. G. Petrenko, Fundamentals of the integrated use of neural network and ontolinguistic paradigms: A comprehensive approach, Cybernetics and Systems Analysis 60 (1) (2024) 111–123. doi:10.1007/ s10559-024-00652-z. [17] H. Jung, W. Kim, Automated conversion from natural language query to SPARQL query, J. Intell.
Inf. Syst. 55.3 (2020) 501–520. doi:10.1007/s10844-019-00589-2. [18] A. Gómez-Pérez, M. Fernández-López, O. Corcho, Ontological Engineering. Advanced Information and Knowledge Processing, 1 ed., Springer-Verlag, London, 2004. doi:10.1007/b97353. [19] R. Studer, S. Staab, Handbook on Ontologies, 2 ed., Springer Berlin Heidelberg, 2009.
doi:10.1007/978-3-540-92673-3. [20] K. Malakhov, M. Petrenko, E. Cohn, Developing an ontology-based system for semantic processing of scientific digital libraries, South Afr. Comput. J. 35.1 (2023). doi:10.18489/sacj.v35i1.1219. [21] O. Palagin, M. Petrenko, K. Malakhov, Challenges and Role of Ontology Engineering in Creating the Knowledge Industry: A Research-Related Design Perspective, Cybern. Syst. Anal. (2024). doi:10.1007/s10559-024-00702-6. [22] O. V. Palagin, An introduction to the class of the transdisciplinary ontology-controlled research design systems, Control Systems and Computers 266 (6) (2016) 3–11. doi:10.15407/usim.2016.06.003. [23] O. P. Kurgaev, O. V. Palagin, Concerning the information support for research, Visn. Nac. Akad.
Nauk Ukr. (2015) 33–48. doi:10.15407/visn2015.08.033. [24] O. V. Palagin, M. V. Semotiuk, Virtualization Technologies Intrinsic to Living Beings, Cybern.
Syst. Anal. (2022). doi:10.1007/s10559-022-00496-5. [25] O. V. Palagin, An Ontological Conception of Informatization of Scientific Investigations,
Cybern. Syst. Anal. 52.1 (2016) 1–7. doi:10.1007/s10559-016-9793-6. [26] O. V. Palagin, O. F. Kurgaev, O. I. Shevchenko, The Noosphere Paradigm of the Development of Science and Artificial Intelligence, Cybern. Syst. Anal. 53.4 (2017) 503–511. doi:10.1007/s10559017-9952-4.