Within the domain of tribology, the science and technology for understanding and controlling friction, lubrication, and wear of relatively moving interacting surfaces, countless experiments are carried out and their results are published worldwide. Due to the variety of test procedures and a lack of consistency in the terminology as well as the practice of publishing results in the natural language, accessing and reusing tribological knowledge is time-consuming and experiments are hardly comparable. However, for the selection of potential tribological pairings according to given requirements and to enable comparative evaluations of the behavior of different tribological systems or testing conditions, a shared understanding is essential. Therefore, we present a novel ontology tribAIn (derived from the ancient Greek word “tribein” (= rubbing) and the acronym “AI” (= artificial intelligence)), designed to provide a formal and explicit specification of knowledge in the domain of tribology to enable semantic annotation and the search of experimental setups and results. For generalization, tribAIn is linked to the intermediate-level ontology EXPO (ontology of scientific experiments), supplemented with subject-specific concepts meeting the needs of the domain of tribology. The formalization of tribAIn is expressed in the W3C standard OWL DL. Demonstrating the ability of tribAIn covering tribological experience from experiments, it is applied to a use case with heterogeneous data sources containing natural language texts and tabular data.
The ontology modeling practice of engineering metaproperties of concepts is a well-known technique. Some metaproperties of concepts describe the dynamics of concept instances, i.e. how instances can and cannot be altered. We investigate how deletions in an ontology-based knowledge base interact with the metaproperties rigidity and dependence. A particularly useful effect are delete cascades. We evaluate how rigidity and dependence may guide delete cascades in an engineering application. A case study in the area of product development shows that beyond explicitly defined deletions, our approach achieves further automated and desirable deletions of facts with high precision and good recall.
When reusing product knowledge in design processes, developers have to decide which knowledge elements are relevant for a task. Thus, mechanisms of knowledge removal are vital for a successful reuse, but are not yet assisted by procedure models. This contribution introduces Intentional Forgetting as a methodology of intelligent removal processes in ontological knowledge bases. The aim is to support developers by providing relevant contents for reuse systematically. The development process of a testrig, that is highly based on knowledge reuse, is considered as use case.
Data-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.
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