Profiling the Web of Data

The Web of Data contains a large number of openly-available datasets covering a wide variety of topics. In order to benefit from this massive amount of open data such external datasets must be analyzed and understood already at the basic level of data types, constraints, value patterns, etc. For Linked Datasets such meta information is currently very limited or not available at all. Data profiling techniques are needed to compute respective statistics and meta information. However, current state of the art approaches can either not be applied to Linked Data, or exhibit considerable performance problems. This paper presents my doctoral research which tackles these problems.