Abstract
This chapter discusses how machine learning techniques can be useful for modelling and understanding context and processes. Machine learning techniques that have been applied for understanding context and processes are briefly presented together with the setting in which they have been applied. An example application focusing on context understanding is described to illustrate results of applying the techniques on real-world data. Interpretation and understanding of context in the ACTIVE knowledge workspace is described in Chap. 5 and deployed at BT as described in Chap. 9, while optimizing and sharing of knowledge processes is addressed in Chap. 6.
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Grobelnik, M., Mladenić, D., Leban, G., Štajner, T. (2011). Machine Learning Techniques for Understanding Context and Process. In: Warren, P., Davies, J., Simperl, E. (eds) Context and Semantics for Knowledge Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19510-5_7
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DOI: https://doi.org/10.1007/978-3-642-19510-5_7
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