## 1 Introduction  
Cyber-physical systems (CPSs) have found their application in many areas of human activity, from home automation to large industrial complexes containing hundreds of systems and subsystems. The growing complexity of the modern CPS forces to cope with challenges such as interoperability, scalability, and increasing volumes of generated data, which leads to the need of new system architectures and frameworks. CPSs [1] have become one of the key elements of the Industry 4.0 concept, which is gaining more and more attention from both academia and industry. For instance, in [2] the authors offer insight on how the concept of Industry 4.0 influences the overall state of manufacturing in Europe, with respect to socio-economical aspects such as improved product manufacturability, reduced production time, and reduced negative impact on the environment. In line with this trend, several key projects have been launched in the framework of the European Horizon 2020 program (e.g., Virtual Factory Open Operating System (vf-OS), DISRUPT, SAFIRE, ENACT, Boost 4.0, and many others) to support the implementation of ideas brought up in Industry 4.0. Smart manufacturing is one of the topics of focus for some of these projects, contributing to a convergence of the digital and physical worlds in the manufacturing domain [3]. The vf-OS project precisely targets issues related to these technologies, enabling the fulfilment of some key requirements and needs of Industry 4.0 by developing a set of modular services for better integration of manufacturing and logistics processes [4]. According to [5], smart manufacturing is moving from knowledge-based manufacturing towards data-driven or knowledge-enabled manufacturing. Thus, the data generated over the entire complex and distributed manufacturing units become a resource that can be utilized to improve the system's efficiency, as well as to find the system's weak and strong points. This makes data processing and analysis a critically important challenge for advanced CPSs, requiring novel Information and communications technology (ICT) support platforms. An important requirement for platform development that supports the ongoing digital transformation in different application domains is modularity, both in terms of the involved technological process and autonomy. Moreover, for platforms developed for Industry 4.0, a hybrid approach can be implemented [6]. This approach assumes that every subsystem or system entity deployed in a certain location performs actions independently, while some form of centralized data assembly is performed. However, the modularity requirement sets several challenges in terms of orchestration and interoperability. For instance, some of the facilities might be dependent on the output from another facility, and data should be \"understandable\" to all parties. Furthermore, with the increasing autonomy and embedded intelligence of components, CPSs are becoming complex networks of collaborative units, leading to the notion of collaborative CPSs [7]. In fact, collaboration issues are at the heart of most challenges in modern industrial systems. The effective materialization of the fourth industrial revolution strongly depends on properly addressing collaborative organizational structures, processes, and mechanisms along the six dimensions of Industry 4.0: vertical integration, horizontal integration, through-engineering across value chain, acceleration of manufacturing, digitalization, and new business models [8]. To better explore the data generated by complex systems, methods and techniques coming from semantic theory are used. Semantic theory assumes that words often appear in text with other related words. This principle can be applied not only in studying text structures but also complex systems such as CPSs, as the language is just \"the reflection of objective reality\" [9]. CPS systems typically contain a lot of heterogeneous components that communicate with each other and generate data.