**6. Conclusions**

Sharing knowledge in publications has a long tradition in scientific research since this is the elemental way of consuming and communicating information and knowledge by human scientists. Within the domain of tribology, the vast amount of available information overcomes the cognitive capacity of humans in terms of efficient aggregation and processing. Therefore, AI provides a lot of potential to support scientists by handling this flood of information. Nevertheless, the way of knowledge sharing within tribology is still mainly based on publications and thus human focused. Descriptions in natural language are vague and insufficient from a formalization perspective. This phenomenon is due to an intended human consumer, for whom the provision of formal sufficient information would result in far too long publications and re-reading the same information over and over again. In contrast, machines need a formal and explicit model for aggregating and processing information. Therefore, if the available amount of information overwhelms the capacity of human processability, the question arises if we better should create representations for sharing information with an AI instead of humans in the future? The answer is: Not necessarily. As pointed out by Gruber [74], "the purpose of AI is to empower humans with machine intelligence". This is referred to as "humanistic AI", an artificial intelligence designed to meet human needs by collaborating with and augmenting people. In terms of an AI empowered tribological knowledge sharing, we introduced a semantic annotation pipeline towards generating knowledge graphs from natural language publications to bridge the gap between a human-understandable and a machine-processable knowledge representation. The pipeline is built upon state-of-the-art NLP methods and is inspired by similar challenges from the biomedical domain. Although we demonstrate the potential of the approach (NER and QA show reliable computational performance scores), further validation of the approach to ensure practical usability is recommended. This includes especially the definition of the specific objective of the extracted information, e.g., for trend studies or identifying research gaps and contradictions within the domain of tribology. Furthermore, the annotation model is currently limited to model tests and is not validated to suit the practical information needs of tribologists. Therefore, user studies for analyzing the capability of information extraction compared with human experts provide possibilities to improve the performance of the approach.

**Author Contributions:** Conceptualization, P.K. and M.M.; methodology, P.K. and R.D.; software, R.D.; validation, R.D. and P.K.; writing—original draft preparation, P.K. and M.M.; writing—review and editing, P.K., M.M., R.D., B.S. and S.W.; visualization, P.K. and M.M.; supervision, B.S. and S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The code for the Semantic Annotation Pipeline is available under https://github.com/snow0815/TriboAnnotation.git, accessed on 14 December 2021.

**Acknowledgments:** P. Kügler, B. Schleich and S. Wartzack gratefully acknowledge the financial support of project WA 2913/22-2 within the Priority Program 1921 by the German Research Foundation (DFG). M. Marian acknowledges the support from Pontificia Universidad Católica de Chile.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

