1st Call for Papers: Knowledge Discovery and Data Mining Meets Linked
Open Data
1st Call for Challenge Entries: Linked Data Mining Challenge 2016
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Fifth International Workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data
(Know@LOD 2016)
including the 4th Linked Data Mining Challenge
Co-located with the 13th ESWC 2016
May 29th - June 2nd 2016, Heraklion, Crete, Greece
http://knowalod2016.informatik.uni-mannheim.de
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The fifth international workshop on Knowledge Discovery and Data Mining
Meets Linked Open Data (Know@LOD) will be held at the 13th 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://easychair.org/conferences/?conf=knowlod2016.
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
Besides research papers, we also invite submission to the 4th Linked
Data Mining Challenge. The challenge consists of a predictive
classification task involving Linked Data entities. Details and
submission instructions can be found at
http://knowalod2016.informatik.uni-mannheim.de/en/linked-data-mining-challenge/.
Important Dates:
Research Papers:
Submission deadline: Friday March 4, 2016
Notifications: Friday April 1, 2016
Camera-ready version: Friday April 15, 2016
Challenge Entries:
Paper and solution submission: Friday March 25, 2016
Notifications: Friday April 1, 2016
Camera-ready version: Friday April 15, 2016
Organization:
Heiko Paulheim, University of Mannheim, Germany
Jens Lehmann, University of Leipzig, Germany
Vojtech Svatek, University of Economics, Prague, Czech Republic
Craig Knoblock, University of Southern California, USA
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Prof. Dr. Heiko Paulheim
Data and Web Science Group
University of Mannheim
Phone: +49 621 181 2661
B6, 26, Room C1.09
D-68159 Mannheim
Mail: [email protected]
Web: www.heikopaulheim.com
