=Paper=
{{Paper
|id=None
|storemode=property
|title=Statistical Machine Learning with Linked Data
|pdfUrl=https://ceur-ws.org/Vol-685/InvitedTalkAbstract.pdf
|volume=Vol-685
}}
==Statistical Machine Learning with Linked Data==
<pdf width="1500px">https://ceur-ws.org/Vol-685/InvitedTalkAbstract.pdf</pdf>
<pre>
 Statistical Machine Learning with Linked Data
                  Talk Abstract

                                  Volker Tresp1

                                  Siemens CT,
                           volker.tresp@siemens.com,

    The size of the Linked Open Data (LOD) cloud is constantly increasing where
the term Linked Data is used to describe a method of exposing, sharing, and
connecting data via dereferenceable URIs on the Web. In this talk we explore the
usefulness of statistical machine learning for LOD. Statistical machine learning
has the chance of exploiting statistical regularities in the data that cannot easily
be captured by logical statements and can handle contradictory, uncertain and
missing data. In general, the data quality on LOD is varying: whereas LOD for
the life sciences has reasonably good quality, other portions of the LOD cloud
are not maintained as well and are still quite noisy. We present existing machine
learning approaches to learning with LOD. We conclude that machine learning
can be quite effective on LOD if the data quality fulfils some minimal quality
requirements.

</pre>