'Knowledge discovery and data mining meets linked open data', Heraklion, Greece
27 May 2012 - 27 May 2012
Greece
An event entitled 'Knowledge discovery and data mining meets linked open data' will take place on 27 May 2012 in Heraklion, Greece.
Knowledge discovery and data mining is a field with a large number of investigatory methods for uncovering of patterns and regularities in large data sets. Research in this field has led to the development of practically relevant and scalable approaches such as association rule mining, subgroup discovery, graph mining, and clustering.
With the growing success of linked data and its use in applications has provided numerous novel opportunities, the scale and heterogeneity of internet data is posing challenges.
The event will look at possible synergies between both the linked data community and the field of knowledge discovery. The event programme will include discussion about how state-of-the-art algorithms for knowledge discovery and data mining could be adapted to fit the characteristics of linked data, such as its distributed nature, incompleteness (i.e. absence of negative examples), and identify concrete use cases and applications.For further information, please visit: http://www.ke.tu-darmstadt.de/know-a-lod-2012/(opens in new window)
Knowledge discovery and data mining is a field with a large number of investigatory methods for uncovering of patterns and regularities in large data sets. Research in this field has led to the development of practically relevant and scalable approaches such as association rule mining, subgroup discovery, graph mining, and clustering.
With the growing success of linked data and its use in applications has provided numerous novel opportunities, the scale and heterogeneity of internet data is posing challenges.
The event will look at possible synergies between both the linked data community and the field of knowledge discovery. The event programme will include discussion about how state-of-the-art algorithms for knowledge discovery and data mining could be adapted to fit the characteristics of linked data, such as its distributed nature, incompleteness (i.e. absence of negative examples), and identify concrete use cases and applications.For further information, please visit: http://www.ke.tu-darmstadt.de/know-a-lod-2012/(opens in new window)