Machine Learning on Linked Data, a Position Paper

Open Access
Authors
Publication date 2014
Host editors
  • I. Tiddi
  • M. d'Aquin
  • N. Jay
Book title Proceedings of the 1st Workshop on Linked Data for Knowledge Discovery
Book subtitle co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014) : Nancy, France, September 19th, 2014
Series CEUR Workshop Proceedings
Event 1st Workshop on Linked Data for Knowledge Discovery
Pages (from-to) 69-73
Number of pages 5
Publisher Aachen: CEUR-WS
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
The combination of linked data and machine learning is emerging as an interesting area of research. However, while both fields have seen an exponential growth in popularity in the past decade, their union has received relatively little attention. We suggest that the field is currently too complex and divergent to allow collaboration and to attract new researchers. What is needed is a simple perspective, based on unifying principles. Focusing solely on RDF, with all other semantic web technology as optional additions is an important first step. We hope that this view will provide a low-complexity outline of the field to entice new contributions, and to unify existing ones.
Document type Conference contribution
Language English
Published at http://ceur-ws.org/Vol-1232/paper7.pdf https://dl.acm.org/citation.cfm?id=3053834
Other links http://ceur-ws.org/Vol-1232/
Downloads
LD4KD2014-Position Paper (Final published version)
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