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1st Call for Papers: Knowledge Discovery and Data Mining Meets Linked Open Data

From: Heiko Paulheim <paulheim@ke.tu-darmstadt.de>
Date: Fri, 01 Feb 2013 17:00:51 +0100
Message-ID: <510BE6B3.80102@ke.tu-darmstadt.de>
To: semantic-web@w3.org, "public-lod@w3.org" <public-lod@w3.org>
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Second International Workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data
(Know@LOD 2013)

Co-located with the 10th Extended Semantic Web Conference (ESWC 2013)
May 26-30, Montpellier, France

http://www.ke.tu-darmstadt.de/know-a-lod-2013/
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After last year’s successful debut, the second international workshop on 
Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD) 
will be held at the 10th Extended Semantic Web Conference (ESWC).

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.

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.

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, with a selection of the best papers of 
each ESWC workshop appearing in an additional volume edited by Springer. 
Papers must be submitted online via Easychair at
https://www.easychair.org/conferences/?conf=knowlod2013

A selection of the best papers of each ESWC workshops will be included 
in an additional volume edited by Springer.

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

Important Dates:

Submission deadline: March 4th, 2013
Notification: April 1st, 2013
Camera ready version: April 15th, 2013
Workshop: May 26th or 27th, 2013

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, University of Potsdam, Germany
Received on Friday, 1 February 2013 16:01:40 UTC

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