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Know@LOD 2012 : Knowledge Discovery and Data Mining Meets Linked Open Data


When May 27, 2012 - May 27, 2012
Where Heraklion, Crete
Submission Deadline Mar 11, 2012
Notification Due Apr 1, 2012
Final Version Due Apr 15, 2012
Categories    semantic web   linked data   data mining   knowledge discovery

Call For Papers

Call for Papers
1st International Workshop on Knowledge Discovery and Data Mining Meets
Linked Open Data (Know@LOD)
Co-located with the 9th Extended Semantic Web Conference (ESWC 2012), Crete

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 proposed workshop,
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.

Authors of contributed papers are especially encouraged to publish their
data sets and/or the implementation of their algorithms, and to discuss
these implementations and data sets with other attendees. The goal is to
establish a common benchmark that can be used for competitive
evaluations of algorithms and tools.


Submissions have to be formatted according to the Springer LNCS
guidelines. We welcome both full papers (max 12 pages) as well as
work-in-progress and position papers (max 6 pages). Accepted papers will
be published online via CEUR-WS. Papers must be submitted online via

Topics of interest include data mining and knowledge discovery methods
for generating and processing, or using linked data, such as

Automatic link discovery
Event detection and pattern discovery
Frequent pattern analysis
Graph mining
Knowledge base debugging, cleaning and repair
Large-scale information extraction
Learning and refinement of ontologies
Modeling provenance information
Ontology matching and object reconciliation
Scalable machine learning
Statistical relational learning
Text and web mining
Usage mining

In order for accepted papers to appear in the workshop proceedings, at
least one of the authors must register for both the main conference and
the workshop.

Important Dates

Submission deadline: March 11th, 2012
Notification: April 1st, 2012
Camera ready version: April 15th, 2012
Workshop: May 27th or 28th, 2012


Johanna Völker, University of Mannheim, Germany
Heiko Paulheim, University of Darmstadt, Germany
Jens Lehmann, University of Leipzig, Germany
Mathias Niepert, University of Mannheim, Germany

Program Committee

Claudia d’Amato, University of Bari, Italy
Sören Auer, University of Leipzig, Germany
Bin Chen, Indiana University, USA
Weiwei Cheng, University of Marburg, Germany
Ying Ding, Indiana University, USA
Dejing Dou, University of Oregon, USA
Kai Eckert, University of Mannheim, Germany
Tim Finin, University of Maryland, USA
George Fletcher, TU Eindhoven, The Netherlands
Johannes Fürnkranz, University of Darmstadt, Germany
Lushan Han, University of Maryland, USA
Laura Hollink, TU Delft, The Netherlands
Andreas Hotho, University of Würzburg, Germany
Kristian Kersting, University of Bonn, Germany
Craig A. Knoblock, University of Southern California, USA
Daniel Lowd, University of Oregon, USA
Alina Dia Miron, Recognos Romania, Romania
Varish Mulwad, University of Maryland, USA
Rahul Parundekar, Toyota InfoTechnology Center, USA
Axel Polleres, Siemens AG Vienna, Austria
Benedikt Schmidt, SAP Research, Germany
Martin Theobald, Max-Planck-Institute Saarbrücken, Germany

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