[public-lod2] 2nd CfP: Second International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know at LOD 2013)

[Jens Lehmann]

Our workshop may be interesting for LOD2, GeoKnow and AKSW members:

---------------------------------------------------------------------
Second International Workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data
(Know at LOD 2013)

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

Submission deadline: March 4th, 2013

http://www.ke.tu-darmstadt.de/know-a-lod-2013/
---------------------------------------------------------------------

After last year’s successful debut, the second international workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data (Know at 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

-- 
Dr. Jens Lehmann
Head of AKSW/MOLE group, University of Leipzig
Homepage: http://www.jens-lehmann.org
GPG Key: http://jens-lehmann.org/jens_lehmann.asc