The availability of large collections of linked data that can be accessed through public services and search endpoints requires methods and techniques for reducing the data complexity and providing high-level views of data contents defined according to users specific needs. To this end, a crucial step is the definition of data classification methods and techniques for the thematic aggregation of linked data. In this paper, we propose matching and clustering techniques specifically conceived for linked data classification, by focusing on the high level of heterogeneity of data descriptions in terms of the number and kind of their descriptive features.
The development of solutions to effectively browse and explore the huge amount of data actually available in the Linked Data Cloud is getting more and more importance. As a result, automated techniques to generate high-level, concept-based information structures are recently being emerging. The notion of inCloud we proposed is an example in this direction. In this paper, we propose to extend the inCloud construction process with crowdsourced knowledge, to benefit from human knowledge and experience for improving the overall quality of resulting inClouds for linked data exploration.
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