About LD4KD

*** Workshop’s proceedings  are now available online.***


Linked Data have attracted a lot of attention from both developers and researchers in recent years, as the underlying technologies and principles provide new ways, following the Semantic Web standards, to overcome typical data management and consumption issues such as reliability, heterogeneity, provenance or completeness. Many different areas of research, from social media analysis to biomedical research, have adopted these principles both for the management and dissemination of their own data and for the combined reuse of external data sources. However, the way in which Linked Data can be applicable and beneficial to the Knowledge Discovery (KDD) process is still not completely understood. It is therefore worth exploring the question of the benefit of Linked Data principles and technologies for knowledge discovery, together with addressing the new challenges that will emerge from joining the two fields, beyond the traditional data management and consumption issues in KDD.

While one of the most obvious scenarios for using Linked Data in a KDD process is the representation of the underlying data following Semantic Web standards, many other aspects of KDD can benefit from including some elements of Linked Data, as a way to reuse external data or to produce new information that can be easily shared or integrated. Contributions here might range from the mining of Linked Data sources to the use of Linked Data to enrich and integrate local data for the purpose of data preparation, results interpretation or visualisation.

This interdisciplinary workshop will therefore provide a forum for researchers to discuss and investigate established as well as potential avenues for interaction and cross-fertilisation between the two fields of Knowledge Discovery and Linked Data, both considered broadly. It will be an opportunity for practitioners of both fields to create communication and collaboration channels, in which they will be able to share their experience and bridge the gap between these overlapping, but mostly isolated communities.


This workshop is kindly supported by www.KDnuggets.com  :  Analytics, Data Mining, & Data Science Resources