1st Call for Papers and Challenge Entries: Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD 2016)

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

-- 
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: heiko@informatik.uni-mannheim.de
Web: www.heikopaulheim.com

Received on Monday, 11 January 2016 11:21:34 UTC