## 1 Introduction
In order to perform reasoning actions over the collected data, there is a need to understand the interdependencies among different objects and concepts, which can be supported by a knowledge base. An example can be found in [10], where authors established a knowledge base that allowed mapping of functional blocks of different standards in the automation domain. Structured data can be efficiently represented through ontologies; in turn, these structures can be represented as a graph [11]. The importance of ontologies for automated web service composition and provision is underlined in [12]. The main goal of an ontology is to provide structural representations of concepts, properties, and the relations among them. However, this representation is largely dependent on the purpose of the ontology. For instance, the \"chair\" and \"lemon\" concepts could be interrelated if they are sold in the same shopping mall. Thus, for text analysis alone, it might be not enough to rely on the co-occurrence of words [9]. However, in this work, we mostly focus on semantic knowledge graphs, which are, according to [13], \"extensive networks of objects or concepts with properties and semantic types, and relationships between objects/concepts providing information about a specific domain\". However, the proposed approach is not limited to a specific domain; rather, it allows integration of various ontologies from various application areas. The issue of data interoperability, and more specifically semantic interoperability, is also in the scope of this work. Key research challenges in the area of semantic interoperability have been pointed out in [14] such as: (i) data modeling and exchange, (ii) ontology matching and merging, (iii) data/event semantic annotation, (iv) knowledge representation, (v) knowledge sharing, (vi) knowledge revision, (vii) semantic discovery of data and services, (viii) semantic routing and publishing/subscribing, (ix)reasoning, and so on. Furthermore, the issue of storage can be added to this list of challenges [15], especially in the context of data-rich environments. Most of the efforts presented in this article focus on semantic discovery, storage, and ontology matching and merging. In the context of this research, semantic discovery is performed through linking the concepts from different ontological models, which, by analogy with semantic routing, have higher preferences or reputation indexes [16]. As such, the main research questions addressed in this work are:  
* Which tools and approaches can contribute to the improvement of semantic interoperability of heterogeneous data? * How can the import and combination of various data models and ontologies help to enrich the mapping process? * How does a collaborative approach contribute to forming the knowledge base and improving the mapping process? To underline the core ideas highlighted in this work, the following topics are addressed: limitations of current semantic discovery architectures, definitions clarifying the difference between semantic similarity and contextual representation, description of the graph-based approach and its advantages over the vector-based approach, semantic distance as an identifier of concept closeness, application of pathfinding algorithms to map data models of concepts, utilization of a weighted approach allowing tracking of evolving semantic relations, and identification of the need to import and integrate ontologies from different sources in order to enrich the knowledge base. The remainder of this article is organized as follows. Section 2 is devoted to a brief discussion of relevant literature, and then the connection of this work to vf-OS is drawn in Section 3. Section 4 is devoted to the semantic management component of vf-OS and some relevant services that are delivered by this component. The next section covers a test-case scenario of how the semantic management component can be used, and related details are discussed. At the end of the article conclusions are made.