## 2 Related Literature
Another work [24] presents a study of the semantic enrichment of manufactured products, with the main objective to enhance the generation and circulation of product-related knowledge among all involved parties. This work departs from these ideas and goes deeper into the subject of data harmonization, including clustering, when basic terms and concepts are combined based on the similarities they possess and are organised based on their semantic and contextual relationships. The use of ontologies to represent machine behavior through the interrelation of input signals and output controls is also discussed in [25]. It is important to distinguish between semantic similarity and contextual representation. According to [26], semantic similarity is a broad term, which considers not only synonyms, but also meronymy (PC and keyboard, keyboard is a part of the PC) and hyponyms (animal and dog, dog is a part of the animal class). Whereas, contextual representation is described [27] as knowledge about how the word is used (i.e., an association between concepts and some attributes common for a specific context). The solution proposed in this work is intended to provide a broader basis for further reasoning, when two concepts might be interrelated or associated not only based on belonging to the same class or one concept being part of another, but also how these two concepts are used together. As an example, we can consider a smart home, where a smart fridge is located in the \"kitchen\", and a mobile vacuum cleaner can move among different rooms, including the \"kitchen\"; thus, the smart fridge might be associated with the vacuum cleaner based on a common location, even if temporarily. This aspect raises another modeling challenge, as some of the concepts or entities might have strong or weak associations indicating how close they are related to each other. Regarding the issue of data representation, two main approaches are discussed in the literature [28]: vector-based and graph-based approaches. The graph-based approach has been chosen for this work based on the following reasons:  
* it can be used to represent order and structural features of the considered topic;
* it allows decomposition of complex topics and representation of interdependencies among subtopics;
* it allows hierarchical representation of topics, features, and so on; and
* it offers pattern detection capabilities. Additionally, in Table 1 we include other literature sources devoted to other aspects that are covered in this work, namely (i) ontology or data model integration, (ii) delivering concept \"relatedness\",(iii) generation of queries for further discovery and visualization, (iv) collaborative knowledge base formation, (v) mapping of data models and ontologies, and (vi) consideration of weighted edges. Most of the works mentioned in Table 1[29; 30] are focused on mapping different data structures, models, and ontologies in order to represent them as graphs in a graph database, but the core logic is maintained [31]. The often-used format for ontology representation is the resource description framework [32; 33], sometimes extended by the web ontology language [27; 29]. Articles devoted to identifying \"relatedness\" of concepts to each other using the path between them do not cover the weighted approach [29; 30; 34; 35]. However, in [36] the authors raise the issue of using the weighted approach for text categorization, whereas each document is considered as a graph to apply a categorization algorithm. This allows identifying the importance of the term based on co-occurrence. In our work, the weighted approach is used to provide a basis for reasoning systems by enabling detection of the importance of the concepts inside the data models to each other. Most current work in the literature is rather focused on contextual representation, considering \"relatedness\" identification rather than semantic similarity [33], which allows interrelating the concepts without a need for predefined knowledge about synonyms, hyponyms, or meronymy.