## 5 Test Case Scenario and Discussion  
The goal of this work is to enable a knowledge base that can accumulate various ontologies and data models, whereas a solution to the main challenges of semantic discovery architectures can be provided [18]. This can be accomplished by considering weighted ontologies and adaptive techniques for collaborative knowledge base formation. Therefore, the aim is not to improve the timing of existing methods and solutions, but rather to focus on functionality. Another goal is to show how the export of ontologies in conjunction with collaborative knowledge base formation can contribute to improving the discovery mechanisms. As a proof-of-concept test case, we decided to import an ontology developed in the framework of the Federated Interoperable Semantic IoT Testbeds and Applications (FIESTA)-IoT project, which aims at covering the core notions of the IoT [53]. The ontology uses the resource description framework (RDF) graph vocabulary and schema with some core constructs such as class, property, type, subClassOf, subPropertyOf, domain, and range [54]. Moreover, web ontology language (OWL) syntax is integrated into the RDF schema to extend its functionality and thus enrich it with new meanings of triples [55]. To implement the mapping, the semantic plugin for Neo4j was used [56], which ensures proper export of the ontology into the database. Besides the RDF graph extended with OWL syntax as the input for the semantic management component, as described in previous sections, the input model in JSON format was used. For testing purposes, importOntology and getMappingSuggestion services were chosen to demonstrate how the importOntology service can contribute to knowledge base enrichment, improvement of the mapping output, and introducing collaborative mechanisms for improving the knowledge base. Moreover, a second purpose is to demonstrate the capabilities of the combined use of getMappingSuggestion with importOntology. First, the importOntology service was executed, which loads the ontology to the graph DB. In the next stage, getMappingSuggestionService was activated to produce a complex mapping result with a set of suggestions for each concept in the origin submodel of the input document (Figure 7). Figure 7: High-level view of the usage of the semantic management component and data integrator. The input model, as described above, contains two main parts, the origin submodel and the target submodel, which are mapped. Following the definitions described in Section 4, the input model that is used for the test case is illustrated in Figure 8. This origin model is represented through set \(A=\{\textit{Manufacturing},\textit{Conveyor},\textit{Emergency Stop},\textit{Company\_X},\textit{ Lisbon}\}\) and the target set as \(B=\{\textit{CoffeeMachine},\textit{Assembly},\textit{Company\_Y},\textit{ Porto},\textit{Speed Monitor}\}\). Each origin concept is mapped to every target concept and produces a suggestion that is ranked according to the weight of the path between the origin and target concepts:  
\[ \begin{array}{l}A\times B=\{(\textit{Manufacturing},\textit{CoffeeMachine}),( \textit{Manufacturing},\textit{Assembly}),\\ (\textit{Manufacturing},\textit{Company\_Y}),(\textit{Manufacturing},\textit{Porto}),( \textit{Manufacturing},\\ \textit{Speed Monitor}),\ldots,(\textit{Lisbon},\textit{CoffeeMachine}),(\textit{ Lisbon},\textit{Assembly}),\\ (\textit{Lisbon},\textit{Company\_Y}),(\textit{Lisbon},\textit{Porto}),( \textit{Lisbon},\textit{Speed Monitor})\}.\end{array}  \tag{15}\]  
In the origin and target models, there are several IoT resources and services, but also location names and company names. In an extended ontology (not in FIESTA ontology), companies can be considered as users or service providers. Using the output from the semantic management component, further knowledge extraction can be made, and \"optimal\" mapping patterns can be found.