=Paper=
{{Paper
|id=None
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-992/preface.pdf
|volume=Vol-992
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-992/preface.pdf</pdf>
<pre>
Know@LOD 2013
The second international workshop on Knowledge Discovery and Data Mining Meets Linked
Open Data (Know@LOD) was held at the 10th Extended Semantic Web Conference (ESWC) in
Montpellier, France. The organizers want to thank the program committee members, authors,
and participants for making this first edition of the workshop a great success. We specifically
want to thank Luc De Raedt for delivering the workshop's keynote speech.


Knowledge discovery and data mining (KDD) is a well-established field with a large
community investigating methods for the discovery of patterns and regularities in large data
sets, including relational databases and unstructured text. 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. At the same time, the Web of Data has
grown to one of the largest publicly available collections of structured, cross-domain data sets.
While the growing success of Linked Data and its use in applications, e.g., in the
e-Government area, has provided numerous novel opportunities, its scale and heterogeneity is
posing challenges to the field of knowledge discovery and data mining:

   •   The extraction and discovery of knowledge from very large data sets;
   •   The maintenance of high quality data and provenance information;
   •   The scalability of processing and mining the distributed Web of Data; and
   •   The discovery of novel links, both on the instance and the schema level.



Contributions from the knowledge discovery field may help foster the future growth of Linked
Open Data. Some recent works on statistical schema induction, mapping, and link mining
have already shown that there is a fruitful intersection of both fields. With the Know@LOD
workshop series, we want to investigate possible synergies between both the Linked Data
community and the field of Knowledge Discovery, and to explore novel directions for mutual
research. We wish to stimulate a 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.
Organization
Johanna Völker, University of Mannheim, Germany
Heiko Paulheim, University of Mannheim, Germany
Jens Lehmann, University of Leipzig, Germany
Mathias Niepert, University of Washington, Seattle, USA
Harald Sack, Hasso Plattner Institute for IT Systems Engineering, Potsdam, Germany




Program Committee
Chris Bizer, University of Mannheim, Germany
Weiwei Cheng, University of Marburg, Germany
Claudia d’Amato, University of Bari, Italy
George Fletcher, TU Eindhoven, The Netherlands
Johannes Fürnkranz, University of Darmstadt, Germany
Agnieszka Lawrynowicz, University of Poznan, Poland
Alina Dia Miron, Recognos Romania, Romania
Axel Polleres, Siemens AG Vienna, Austria
Yves Raimond, BBC, UK
Sebastian Schaffert, SRI, Austria
Benedikt Schmidt, SAP Research, Germany
Martin Theobald, Max-Planck-Institute Saarbrücken, Germany

</pre>