## 4 Discussion  
As demonstrated in the use case, tribAIn allows different experimental data sources to be linked and annotated in the domain of tribology and thus enables the formalization of experimental setups and results for reuse, analysis, and comparison. Moreover, the concepts of the ontology provide an explicit specification of the three core areas of procedure, experimental results, and experimental design. The initial focus of tribAIn is on providing a formal model of the tribological reference method, which is the common basis for comparing the influences of parameters, lubricants, and coatings beyond experimental setups. As argued in the introduction, a main challenge is the accessibility of experimental results in the natural language, which leads to ambiguity in the terminology and makes them hardly comparable. This is even problematic because of the unstructured character of natural language, which is a great barrier using computer-aided processing and analyzing. A first step for the formalization is the ontological annotation within tribAIn by providing explicit concepts as exemplarily shown in the use case. Yet, it should be mentioned that the outlay of manual annotation at this initial state is rather high. Therefore, an (semi-) automatization of semantic annotation and the deployment of text mining techniques will be vital for the future application of tribAIn. This usually commences with the accessibility of non-textual documents like PDF, preprocessing of unstructured text, and goes further to section or concept extraction similar to examples in the field of structural mechanical simulations [36]. In this regard, the performance of text mining and automated annotating strongly depends on the accessibility of suitable training data. Text documents are a common publishing medium for experimental results in the field of tribology. While the shown examples of this contribution focus on the formal representation of experimental results for friction and wear behavior of a tribological system, major tasks also include surface and material characterization. Therefore, some concepts for annotating characterization results, e.g., obtained by analyzing material with microscopy producing image formats, were also included in tribAIn. The annotation of multimedia data is beyond the initial stage of tribAIn and the scope of this contribution and is the subject of ongoing research. To compensate this, the adaption of approaches like COMM [37] will be taken into account. Another issue to deal with is the maintenance and the provision of provenance information for tribAIn. Following the AGM theory [38], there are three change operations, namely expansion, revision, and contraction. Including or removing concepts is vital for the maintenance of tribAIn, especially since the parameter and unit concepts do not have the claim to be complete for all tribological procedures. Changes in an ontology can lead to inconsistencies, which have to be resolved through appropriate methods. Nevertheless, dealing with extensions on the individual level, an OWL ontology provides more flexibility compared with relational approaches due to the open world assumption (OWA). Unlike the closed world assumption (CWA) of common databases, which follow the principle prohibiting everything until it is permitted, OWL permits every assertion until it is prohibited due to OWA. This means the lack of information within an ontology does not imply falsity, it is just unknown. In contrast, CWA assumes everything as false that cannot be proven as true. Therefore, the OWA approach represents an excellent solution for incomplete data as frequently encountered in the domain of tribology [39] due to its complexity of phenomena and testing. Besides this, the complexity of phenomena and testing makes the comparison of experimental setups and results challenging. Within tribAIn, this was faced by integrating concepts from the underlying methodology to generalize individual testing conditions.