- From: Mathieu d'Aquin <m.daquin@open.ac.uk>
- Date: Fri, 11 Apr 2014 09:34:01 +0100
- To: <semantic-web@w3.org>
- CC: Ilaria Tiddi <ilaria.tiddi@open.ac.uk>
** apologies for cross-posting ** ================================ LD4KD 2014 1st Workshop on Linked Data for Knowledge Discovery http://events.kmi.open.ac.uk/ld4kd2014/ co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery 2014 (ECML/PKDD 2014) 15-19 September 2014, Nancy, France (http://www.ecmlpkdd2014.org/ ) ================================ Linked Data have attracted a lot of attention in recent years in many research areas, as their technologies and principles provide new ways to overcome typical data management and consumption issues such as reliability, heterogeneity, provenance or completeness. However, the way in which Linked Data can be applicable and beneficial to the Knowledge Discovery (KDD) pocess is still not completely understood. Many aspects of KDD could benefit from Linked Data, e.g. mining Linked Data sources, using Linked Data to enrich, represent or integrate local data for data preparation, interpretation or visualisation. LD4KD will be an interactive hub to explore the benefits of Linked Data principles and technologies for Knowledge Discovery, together with addressing the new challenges that will emere from joining the two fields. It will be an opportunity for practitioners of both fields to create communication and collaboration channels,and bridge the gap between their overlapping, but mostly isolated communities. The workshop encourages the participation of researchers from the Knowledge Discovery field to discuss and get informed about the use, benefits and challenges of Linked Data, while th Linked Data researchers can take advantage of and adapt Knowledge Discovery methods in their domain. *SCOPE* We welcome high quality position and research papers in which (1) Linked Data are used as support of Knowledge Discovery processes to extract useful knowledge, or (2) Knowledge Discovery techniques are adapted to work and possibly extend Linked Data. Topics of either theoretical and applied interest include, but are not limited to: - Linked Data for data pre-processing: cleaning, sorting, filtering or enrichment - Linked Data applied to Machine Learning - Linked Data for pattern extraction and behaviour detection - Linked Data for pattern interpretation, visualization or optimisation - Reasoning with patterns and Linked Data - Reasoning on and extracting knowledge from Linked Data - Linked Data mining - Links prediction or links discovery using KDD - Graph mining in Linked Data - Interacting with Linked Data for Knowledge Discovery *IMPORTANT DATES* Paper submission deadline: June 20th Notification Of Acceptance: July 20th Camera ready copies due: August 5th, 2014 Workshop date: September 15th/19th, 2014 *SUBMISSIONS* Articles should be written following the Springer LNCS template (see authors instructions at http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0) and can be up to 10 pages in lenght for research papers or 5 pages for position papers, including figures and references. Submissions are exclusively admitted electronically, in PDF format, through the EasyChair system. The submission site is https://www.easychair.org/conferences/?conf=ld4kd * ORGANISING COMMITTEE * Ilaria Tiddi, Knowledge Media Institute, The Open University, UK Mathieu d'Aquin, Knowledge Media Institute, The Open University, UK Nicolas Jay, Orpailleur, Loria, France *CONTACTS* mathieu.daquin@open.ac.uk ilaria.tiddi@open.ac.uk nicolas.jay@loria.fr *PROGRAMME COMMITTEE* Francesca Alessandra Claudia D'Amato Tommaso di Noia Nicola Fanizzi Johannes Fürnkranz Nathalie Hernandez Agnieszka Lawrynowicz Amedeo Napoli Andriy Nikolov Heiko Paulheim Sebastian Rudolph Harald Sack Vojtěch Svátek Isabelle Tellier Cassia Trojahn -- The Open University is incorporated by Royal Charter (RC 000391), an exempt charity in England & Wales and a charity registered in Scotland (SC 038302).
Received on Friday, 11 April 2014 08:34:30 UTC