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The objective of K-NET is to explore the fundamental problem: how different services to manage social interactions in a networked enterprise can be used to enhance knowledge and knowledge management (KM) services.
The key hypothesis of K-NET is that the context under which knowledge is collectively generated and managed can be used to enhance this knowledge for its further use within intra-enterprise collaboration. By extracting the context under which the knowledge is generated in a network (e.g. goals, teams, temporal and spatial aspects), it is possible to enrich it to be more effectively used within future work.
In order to explore such hypothesis, the project intends to answer several problems: how to efficiently monitor/trace a process of generation/usage of knowledge in the network so that this knowledge can be re-used for future work; how to extract context from this process; and how to enrich the knowledge generated with extracted context to support knowledge sharing in future network activities.
By solving these problems, K-NET will allow the development of new services to manage social interactions allowing to effectively monitor the (collaborative) knowledge generation/usage processes (specifically addressing knowledge provided/contained in ’smart‘ devices), services to automatically extract context from such processes and enrich the knowledge, and KM services applying extracted context to support use of this knowledge in the network, with special emphasis on knowledge representation services (considering e.g. IPR and privacy issues).
These services will open new business opportunities for networked enterprises to provide new products/services. K-NET will develop generic services, applicable across different domains, and specifically explore new business opportunities in manufacturing and engineering SMEs. Three demonstrators of the application of new services in real industrial environment and their usage for new business models will be provided.

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K-Net has received funding from the European Union’s RP 7 research and innovation programme under grant agreement no. 215584

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