In this master’s thesis, we focus on one specific business question: “How can we search for compatible components offered by a company using ontologies?” In this context, “we” is a porte-manteau for both internal (company) and external (end) users. We answer this question within the field of digital photography material, for which we have built an ontology together with our partner Sony. Building such an ontology implies a profound understanding of the domain. First of all we look at the semantic annotations on the websites of Sony, and their main competitors, in order to grasp the scope of the domain. This breadth analysis helps us to integrate these existing annotations as a layer into the ontology. In general, these annotations, i.e. properties, are not camera specific, but they represent general features present in every product. We perform a descriptive statistical analysis on this breadth analysis, where we show that the vendors use different annotation schemas. Some vendors like Sony perform well in annotating their data, while others lack even basic support. Our work can be used as a guideline by them to see which schemas are used the most frequent inside the digital camera domain and eventually which schemas they should implement. Next we investigate the naming conventions used by the various vendors in the domain, and we list all possible attributes. We call this the depth analysis, and this results in the specific camera related attributes present in the ontology. For the attributes present in the given dataset, we also encoded the required cardinalities, whenever possible. Sony provided us with a general internal product classification schema which they want to add to our ontology as a business taxonomy. This schema links support files, e.g. a driver or manual, to the respective product. Before we can add the schema to the ontology, we make a domain analysis to better comprehend the business concepts used by our partner. Our partner also provided real world data that we used to populate and test this ontology. The core data is provided as a Microsoft Access database which needs to be cleaned and transformed into triples. These triples are then stored in a designated triplestore before they can be used together with the developed ontology. We conclude our research, by constructing a proof of concept application demonstrating that our ontology combined with the data stored in the triplestore is capable of reasoning over product compatibility. In this application we show, for a given camera model, all direct compatible flashes, lenses, batteries, and tripods. In addition, we also demonstrate indirect compatible lenses, given a camera model. In our context, indirect compatibility is defined as compatibility through an additional adapter. So, in our demo this means that lenses are compatible via a mount, i.e. lens, adapter. We discuss various issues and limitations that we encountered during our work, and we consider potential solutions for them. These issues are investigated with respect to a real world implementation of our application. Finally, we elaborate on future work and possible extensions.