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Deadline Extension: 1st International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD)

From: Heiko Paulheim <paulheim@ke.tu-darmstadt.de>
Date: Fri, 24 Feb 2012 17:36:10 +0100
Message-ID: <4F47BC7A.5000006@ke.tu-darmstadt.de>
To: semantic-web@w3.org, public-lod@w3.org
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2nd 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
http://www.ke.tu-darmstadt.de/know-a-lod-2012/
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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

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 easychair.

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 (extended): March 11th, 2012
Notification: April 1st, 2012
Camera ready version: April 15th, 2012
Workshop: May 27th or 28th, 2012

Organization

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
Received on Friday, 24 February 2012 16:36:48 UTC

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