Collective Intelligence Systems Research Articles

A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science.

The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

MODELS OF THE SYSTEM OF COLLECTIVE SELF-ORGANISATION OF UNMANNED AERIAL VEHICLES USING ARTIFICIAL INTELLIGENCE

The article examines the current state and trends in the development of unmanned aerial vehicles (hereinafter - UAVs), methods and means of their control, self-organisation, flight routing, as well as the involvement of artificial intelligence technologies in the UAV teams system. The main trends in the use of UAVs in the defence sector are analysed. The key problems in this area are highlighted, as well as the shortcomings of existing methods and systems. The article analyses scientific works of domestic and foreign scholars, studies problematic issues and suggests ways of their solution. The introduction of artificial intelligence into the UAV control system has significant potential and makes the development of these technologies urgent. The development of UAV collective control systems, including those using artificial intelligence, allows for the effective use of technology in various fields, ensuring increased coordination, functionality and overall efficiency. At the same time, despite active research in this area, a number of problems related to the development of methods and algorithms for group work still remain unresolved. The issue of integrating information technologies created on their basis and the specifics of their implementation in collective intelligence systems in order to increase the efficiency of solving complex formalised tasks is not sufficiently studied. A method of self-organisation of the UAV team with decentralised control is proposed, in which a number of functions (route planning, distribution of roles, determination of optimal actions, obtaining and processing information) assigned to the onboard control system of the robotic air complex can be performed by each element of the UAV team system due to their self-organisation. The practicality of this method lies in the fact that the UAV's artificial intelligence will constantly self-learn and improve, and if necessary, can be reprogrammed to meet the required conditions of the task. As a consequence, the results and time required to complete missions will improve significantly, while the number of control operators will decrease. It solves a number of problems and shortcomings related to the organisation of the management system, route planning, distribution of roles, speed and completeness of receiving, processing and transmitting information, which in turn improves the security and performance of the system.

Harnessing AI and analytics to enhance cybersecurity and privacy for collective intelligence systems.

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.

Contextuality in Collective Intelligence: Not There Yet.

Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously, it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted by using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of the intransitivity, these simulated colonies came close to but failed to violate Dzhafarov's inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required.

Nanorobot-Mediated Synchronized Neuron Activation.

Interactions between active materials lead to collective behavior and even intelligence beyond the capability of individuals. Such behaviors are prevalent in nature and can be observed in animal colonies, providing these species with diverse capacities for communication and cooperation. In artificial systems, however, collective intelligence systems interacting with biological entities remains unexplored. Herein, we describe black (B)-TiO2@N/Au nanorobots interacting through photocatalytic pure water splitting-induced electrophoresis that exhibit periodic swarming oscillations under programmed near-infrared light. The periodic chemical-electric field generated by the oscillating B-TiO2@N/Au nanorobot swarm leads to local neuron activation in vitro. The field oscillations and neurotransmission from synchronized neurons further trigger the resonance oscillation of neuron populations without synaptic contact (about 2 mm spacing), in different ways from normal neuron oscillation requiring direct contact. We envision that the oscillating nanorobot swarm platforms will shed light on contactless communication of neurons and offer tools to explore interactions between neurons.

Massive Shape Formation in Grid Environments

Shape formation mechanism plays an essential role in many natural processes, involving the formation and evolution of living or non-living structures, and shows potential applications in many emerging domains. In existing research and practice, there still lacks a shape formation mechanism that manifests efficiency, scalability, and stability at the same time. Inspired by phototaxis observed in nature, we propose a self-organized approach for the massive formation of connected shapes in grid environments. The key component of this approach is an artificial light field superimposed on a grid environment, which is determined by the positions of all agents and at the same time drives all agents to change their positions, forming a dynamic mutual feedback process. To evaluate the effectiveness of this approach, we conduct a set of simulations, involving <inline-formula> <tex-math notation="LaTeX">$156$</tex-math> </inline-formula> shapes from <inline-formula> <tex-math notation="LaTeX">$16$</tex-math> </inline-formula> categories, comparing with four baseline methods. The results show that: (1) our approach outperforms the three semi-/decentralized non-optimal baselines in efficiency, scalability, and stability; (2) compared to the centralized optimal baseline, our approach exhibits considerable decreases in the absolute completion time on diverse shape formation tasks, indicating a better efficiency and scalability of our approach. <i>Note to Practitioners</i>&#x2014;In nature, shape formation phenomena emerge from collective behaviors of swarms based on chemical or physical signals. These natural phenomena provide valuable insights to build large-scale multi-agent collaboration systems using software-defined digital signals. This work proposes a phototaxis-inspired computational approach for shape formation that enables a massive swarm of agents to form arbitrary connected shapes in grid environments based on a digital signal called artificial light field. The significance of this work is twofold: 1. it could contribute to a deep understanding of shape formation mechanisms; 2. it would motivate new research on advanced multi-agent algorithms, massive collaboration mechanisms, and artificial collective intelligence systems and facilitate their practical applications. Specifically, the shape formation mechanism has promising applications, including smart warehouses, autonomous cooperation of UAVs, and intelligent transportation systems. A possible realistic application scenario of our method in intelligent transportation systems is bike sharing systems, in which the designated parking areas for shared bicycles near the work area are often overcrowded and difficult to park in during the morning peak period, so dynamic parking route guidance for users is required. Our method can directly apply to this scenario by utilizing the light field to represent the parking state of nearby bicycles and guiding the moving direction of each user.

Building Global Societies on Collective Intelligence: Challenges and Opportunities

Digital disruptions caused by the use of technologies like social media arguably present a formidable challenge to democratic values and in turn to collective intelligence. Challenges such as misinformation, partisan bias, polarization, and rising mistrust in institutions (including mainstream media) present a new constant threat to collectives both online and offline—amplifying the risk of turning ‘wise’ crowds ‘mad’ and rendering their actions counterproductive. Considering the increasingly important role crowds play in solving today’s socio-political, technological, and economical issues, and in shaping our future, it is vital to protect crowd-oriented systems against such disruptions. In this commentary, we identify time-critical challenges and potential solutions from emerging work on diversity, transparency, collective dynamics, and machine behavior that require urgent attention if future collective intelligence systems are to sustain their indispensable role as global deliberation instruments.

Identification method for collective consensus mechanism based on inverse reinforcement learning

<p indent="0mm">Collective intelligence, an important topic of the new generation of artificial intelligence, is a significant way to solve large-scale and complex problems in an open environment. It is crucial in other branches of artificial intelligence. Agents interact and evolve in response to the consensus mechanism to reach a group consensus. Identifying the consensus mechanism is essential for building and comprehending the collective intelligence system. Traditional consensus mechanism modeling methods require many simplified assumptions, and meeting the challenge of complex collective intelligence systems is difficult. A method for identifying consensus mechanisms based on data should be developed. In this paper, the consensus mechanism’s identification problem is transformed into an inverse reinforcement learning problem for the collective intelligence system. We proposed inverse reinforcement learning methods for collective systems and evaluated them in two tasks. The results indicate that the proposed methods can identify the policy function and the reward function of the collective system.

Philosophy 2.0: Applying Collective Intelligence Systems and Iterative Degrees of Scientific Validation

Methods of improving the state and rate of progress within the domain of philosophy using collective intelligence systems are considered. By applying mASI systems superintelligence, debiasing, and humanity’s current sum of knowledge may be applied to this domain in novel ways. Such systems may also serve to strongly facilitate new forms and degrees of cooperation and understanding between different philosophies and cultures. The integration of these philosophies directly into their own machine intelligence seeds as cornerstones could further serve to reduce existential risk while improving both ethical quality and performance.

Integrating Big Data Analytics, Systems Thinking and Viable Systems Approach Towards a Shift from Individual to Collective Intelligence and Collective Knowledge Systems

The growing complexity of social systems and the fast technology evolution make central the role of innovative information technologies in complex organisations geared towards collective intelligence processes among the various social actors and analytical tools. These are able to foster participants’ knowledge, manage the feedback through a holistic approach and, hence, shift organisations from a plurality of voices to an interactive intelligence representing the ultimate identity of the organisation itself. In this regard, this paper aims to offer a model for managing external and internal knowledge to support the viability of the organisation (system) in the longer term. The study adopts the interpretative lens provided by Systems Thinking, System Dynamics and Viable System Approach (vSa) to investigate the challenging domain of knowledge and information management for complex systems such as organisations. Therefore, a qualitative and interpretative approach has been chosen to reflect upon Big Data approaches and Collective Knowledge Systems (CKS), embracing a system perspective. The proposed conceptual model shows the crucial role played by the holistic managing of the external and internal knowledge that permits the alignment of the information variety of the organisation to the context and the entities that compose it in order to create harmonic relations. Leveraging on the concepts of vicariance, bricolage and exaptation, several advantages emerge that correlate to the complex system’s ability to reach a greater level of survival by adapting and dynamically evolving itself. The ensuing investigation shows how Systems Thinking and Viable System Approach can provide deep insights into the field of information technology, evidencing the systems thinking contribution in analysing, understanding and managing dimensions and paths of social dynamics. A contribution to previous studies is provided with reference to themes such as Big Data, information and knowledge management.

Paving the Way Towards Collective Intelligence at the IoT Edge

With the scale of data generated and the increasing computing capabilities at the IoT Edge, moving intelligence to end-devices and gateways in IoT networks is within the realms of possibility. There are tremendous hopes on how self-awareness in IoT networks could be helpful to solve a variety of smart city use case problems. Self-awareness implies self-teaching; the network devices autonomously become smarter with AI. Collective intelligence is a subset of self-awareness but not yet formalized and exploited at the Edge of IoT networks. In this paper, current mathematical models for Collective Intelligence are studied and compared. Their suitability for on-device machine learning deployment is also explored. Moreover, it attempts to lay down a framework using graph theory coupled with artificial neural networks to model a collective intelligence system applicable to a specific scenario.

Contextuality in Neurobehavioural and Collective Intelligence Systems

Contextuality is often described as a unique feature of the quantum realm, which distinguishes it fundamentally from the classical realm. This is not strictly true, and stems from decades of the misapplication of Kolmogorov probability. Contextuality appears in Kolmogorov theory (observed in the inability to form joint distributions) and in non-Kolmogorov theory (observed in the violation of inequalities of correlations). Both forms of contextuality have been observed in psychological experiments, although the first form has been known for decades but mostly ignored. The complex dynamics of neural systems (neurobehavioural regulatory systems) and of collective intelligence systems (social insect colonies) are described. These systems are contextual in the first sense and possibly in the second as well. Process algebra, based on the Process Theory of Whitehead, describes systems that are generated, transient, open, interactive, and primarily information-driven, and seems ideally suited to modeling these systems. It is argued that these dynamical characteristics give rise to contextuality and non-Kolmogorov probability in spite of these being entirely classical systems.

A proposed model for disclosing the role of the collective intelligence system in improving joint auditing

This research aims to present a proposed model for disclosure and documentation when performing the audit according to the joint audit method by using the questions and principles of the collective intelligence system, which leads to improving and enhancing the efficiency of the joint audit, and thus enhancing the confidence of the parties concerned in the outputs of the audit process. As the research problem can be formulated through the following question: “Does the proposed model for disclosure of the role of the collective intelligence system contribute to improving joint auditing?” &#x0D; The proposed model is designed for the disclosure of joint auditing and the role of collective intelligence in improving it to achieve integration between the auditor’s report on the one hand and the joint audit information on the other hand, by disclosing the joint audit information in the explanations complementing the audit report that should be available in the current audit file of the economic unit in question. Auditing, by merging the questions of the collective intelligence system (who, what, how, why) with the indicators of the quality of the audit, and the research reached a set of conclusions, the most important of which is unified documentation of the joint audit work in the audit office as it is permissible to use the collective intelligence system—documenting the work carried out by members of his team independently of the other office. As for the most important recommendations, they were represented in need to adopt the proposed model for using collective intelligence to improve the quality of joint auditing performance, which aims to provide a mechanism for disclosure and documentation of joint auditing

Искусственный интеллект и интеллектуальная собственность

In the article, prepared on the basis of the author’s plenary report presented at the XII International Forum “Innovative Development through the Intellectual Property Market”, held in Moscow at the MSLA on October 30, 2020, issues arising in the field of intellectual property rights in connection with the development of artificial intelligence systems and their impact on the development of legal relations in the economy and culture of modern society are considered. Aspects of mutual policies in the field of intellectual property rights and the development of artificial intelligence systems for the development of innovation and creativity are discussed. Questions of copyright and ownership are raised and proposed in the interaction of man, collective and artificial intelligence or artificial intelligence systems. Issues related to artificial intelligence as an object of intellectual property are considered. The position of the author on the legal personality of artificial intelligence to intellectual property objects created by autonomous artificial intelligence systems is presented, which is expressed in the answers to the questions of the project of the World Intellectual Property Organization to the wide discussion of interested parties, planned for 2020 at the headquarters of the World Intellectual Property Organization in Geneva. The main conceptual principle of the author on the issues of the planned discussion is to grant the right of copyright and ownership of intellectual property objects created by autonomous artificial intelligence to a dressed subject — a person or collective, a developer of artificial intelligence with fixation of the latter as a sub-subject or instrument of the subject. Traditional categories of intellectual property rights are also discussed, such as patentability and the inventive level of property in connection with the possible generation of these objects by artificial intelligence. Issues related to data, its generation, fabrications and legal relations regarding data are considered. It is proposed to harmonize international intellectual property rights policies to reduce the technological gap between countries in the context of artificial intelligence development.

Collective Intelligence Algorithms in Pedagogical Practice

The article considers the ideas of using artificial intelligence algorithms in pedagogics. It presents the methodology of the so-called collective pedagogical megasystem. The introduction of such an ephemeral construct is necessary only to understand the collective pedagogical intelligence system, formulate it in a model, find out its operation patterns and the laws it obeys. It would contribute to predicting pedagogical processes and phenomena and formulating new laws. The objective of the article is to demonstrate the application of collective intelligence algorithms in pedagogical practice for effective didactic decision-making. The matter is that in a real educational process, besides the well-known set of pedagogical conditions, there are some random and unpredictable reasons and factors that are hard to foresee or anticipate. Due to their stochastic nature, they occur spontaneously. These single reasons make a minor impact on the teaching methods selection, but in aggregate their influence gets so strong that they can upturn some prognostic conclusions. The problem also focuses on identifying the factors that would ensure the highest efficiency and productivity of studies among the known (expected) and random (unexpected) reasons. For these purposes, the most suitable algorithm for the selection training methods is the so-called ant algorithm which, on the one hand, considers the randomness of the influence parameters, and on the other, ensures steady and high productivity. A certain example was selected to demonstrate the process of applying the ant algorithm to reveal the best hierarchy of the pedagogical conditions (factors) that determines the optimum choice of the training method. The authors conclude that human intelligence is distributed and integrated at the same time, and the application of collective intelligence algorithms in pedagogical practice can yield some effective didactic decisions

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web-application was used to crowd-source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web-application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO2/min (11.1%, r = 0.97) and 144 (149) mlO2/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET.

Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management

This paper proposes a framework based on the collective intelligence principle to understand how the healthcare ecosystem is facing the challenges posed by the COVID-19 by using the Internet of Things (IoT) combined with other digital technologies. The underlying assumption is to consider the Healthcare ecosystem as a collective intelligence system in which the multitude of actors can be coordinated to address the pandemic-specific management challenges. The Italian healthcare ecosystem is analyzed as scenario taking in consideration the ‘genes’ of the collective intelligence: What is being done?, Who is doing it?, Why are they doing it? and How is it being done?. Our analysis introduces policy implications based on a unique decision support system (DSS) to allocate a limited set of IoT devices to a larger group of patients, to balance the alternative needs to improve the conditions of the most severe patients but to maximize the efficiency of device use.

Integrating Big Data Analytics, Systems Thinking and Viable Systems Approach Towards a Shift from Individual to Collective Intelligence and Collective Knowledge Systems

Purpose – The growing complexity of social systems and the faster technology evolution make central the role of the innovative information technology in complex organisations, geared towards collective intelligence processes among the various social actors and analytical tools. These are able to foster the participant knowledge, manage the feedback through a holistic approach and hence shift organisations from a plurality of voices to an interactive intelligence representing the ultimate identity of the organisation itself. In this regard, the aim of this paper is to offer a model for managing external and internal knowledge in order to support the viability of the organisation (system) in the longer term. Design/methodology/approach – The paper adopts the interpretative lens provided by Systems Thinking, System Dynamics and Viable System Approach (vSa) to investigate the challenging domain of the knowledge and information management for complex systems as organisations. Therefore, a qualitative and interpretative approach is carried out to reflect upon Big Data approaches and Collective Knowledge Systems (CKS), embracing a system perspective. Findings – The proposed conceptual model shows the crucial role covered by the holistic managing of the external and internal knowledge that permits to align the information variety of the organisation to the context and the entities that compose it in order to create harmonic relations. Leveraging on the concepts of vicariance, bricolage and exaptation, several advantages emerge that are correlated to the capacity of the complex system to reach a greater level of survival, by adapting and dynamically evolving itself. Originality/value – The paper shows how Systems Thinking and Viable System Approach can provide deep insights into the field of information technology, evidencing the systems thinking contribution in analysing, understanding and managing dimensions and paths of social dynamics. A contribution to previous studies is provided with reference to themes as Big Data, information and knowledge management.

Applying Independent Core Observer Model Cognitive Architecture to a Collective Intelligence System

This paper shows how the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) can be applied to building a collective intelligence system called a mediated Artificial Superintelligence (mASI). The details include breaking down the ICOM implementation in the form of the mASI system and the general performance of initial studies with the mASI. Details of the primary difference between the Independent Core Observer Model Cognitive Architecture and the mASI architecture variant include inserting humanity in the contextual engine components of ICOM, creating a type of collective intelligence. Humans can ‘mediate’ new system-generated thinking keeping the thought process accessible and slow enough for humans to oversee and understand. This also allows the modification of emotional valences of the thought process of the mASI system to help the system generate complex contextual models (knowledge graphs) of new ideas and which speeds up the learning process. With the humans acting as control rods in a reactor and emotional drivers, the mASI system maintains safety where the system would cease to function if humans walked away.

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however, teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multi-aspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the “e-tourism” domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a human-machine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.