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. Chris Bizer 0 University of Mannheim 0 Germany Weiwei Cheng 0 University of Marburg 0 Germany Claudia d'Amato 0 University of Bari 0 Italy George Fletcher 0 TU Eindhoven 0 The Netherlands Johannes Fürnkranz 0 University of Darmstadt 0 Germany Agnieszka Lawrynowicz 0 University of Poznan 0 Poland Alina Dia Miron 0 Recognos Romania 0 Romania Axel Polleres 0 Siemens AG Vienna 0 Austria Yves Raimond 0 UK Sebastian Schaffert 0 Austria Benedikt Schmidt 0 SAP Research 0 Germany Martin Theobald 0 Max-Planck-Institute Saarbrücken 0 Germany 0 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

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.

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Program Committee