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Know@LOD 2014 : 3rd International Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data


When May 25, 2014 - May 25, 2014
Where Crete, Greece
Submission Deadline Mar 6, 2014
Notification Due Apr 1, 2014
Final Version Due Apr 15, 2014
Categories    semantic web   linked data   data mining   artificial intelligence

Call For Papers

Third International Workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data
(Know@LOD 2014)

Co-located with the 11th Extended Semantic Web Conference (ESWC 2014)
May 25-29, Crete, Greece

The third international workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD) will be held at the 11th 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

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 6th, 2014
Notification: April 1st, 2014
Camera ready version: April 15th, 2014
Workshop: May 25th or 26th, 2014


Johanna Völker, University of Mannheim, Germany
Jens Lehmann, University of Leipzig, Germany
Heiko Paulheim, University of Mannheim, Germany
Harald Sack, University of Potsdam, Germany
Voijtech Svatek, University of Economics, Prague, Czech Republic

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