Abstract
The reuse of existing information is an important factor for efficient business workflows in mechanical engineering. However, a considerable amount of this information is currently stored in unstructured text documents and therefore not easily accessible. Currently, no existing software system gives access to this hidden information. Companies will benefit from using linguistic algorithms in combination with semantic technologies to extract information from unstructured documents. This article exemplifies the successful use of semantic technologies, combined with textmining tools to extract facts from unstructured data. An example from the field of automation industry is used to describe and evaluate the presented approach. A function-based knowledge model for the classification and description of technical solutions is developed. An innovative software system supports the annotation of unstructured documents and the subsequent search for technical solutions. Previously inaccessible and hidden knowledge is unlocked and can be utilized more efficiently in business processes.
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Hesse, HJ., Kohn, A., Lehmann, J., Farzaneh, H.H., Ihlenburg, D. (2014). Integration of Semantic Technologies for Business Process Support in the Automation Industry. In: Wahlster, W., Grallert, HJ., Wess, S., Friedrich, H., Widenka, T. (eds) Towards the Internet of Services: The THESEUS Research Program. Cognitive Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-06755-1_31
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DOI: https://doi.org/10.1007/978-3-319-06755-1_31
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