- From: Heiko Paulheim <heiko@informatik.uni-mannheim.de>
- Date: Mon, 11 Jan 2016 12:21:12 +0100
- To: SW-forum <semantic-web@w3.org>, "public-lod@w3.org community" <public-lod@w3.org>
1st Call for Papers: Knowledge Discovery and Data Mining Meets Linked Open Data 1st Call for Challenge Entries: Linked Data Mining Challenge 2016 --------------------------------------------------------------------- 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 --------------------------------------------------------------------- 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