Linked Data have attracted a lot of attention 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.
The LD4KD2015 workshop is meant to be an opportunity to improve your own knowledge on this new interdisciplinary topic, raising your own questions, and present new challenges and issues you have experienced in your research. We intend to create communication and collaboration channels, in order to be able to share experiences and reduce the gap between these overlapping, but still isolated communities.
Workshop activities and program
- Linked Data/KDD Question&Suggestions: Share your ideas here
- Knowledge Discovery tools using Linked Data: add/remove tools here
10:00 – 10:15 Welcome
10:15 – 10:45 Mathieu d’Aquin, Linked Data for Knowledge Discovery: introduction
11:15 – 11:30 Coffee Break
12:00 – 12:45 Ilaria Tiddi, Knowledge Discovery tools integrating Linked Data
12:45 – 13:00 Wrap-up and conclusions
Paper submission deadline: Monday, June 22, 2015 extended Monday , June 29th Paper acceptance notification: Monday, July 13, 2015 Camera-ready deadline: Monday July 27, 2015
Workshop day: Friday September 11, 2015
We welcome high quality research papers, position papers and demos 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, visualisation or optimisation
- Reasoning with patterns and Linked Data
- Reasoning on and extracting knowledge from Linked Data
- Linked Data mining
- Links prediction or link discovery using KDD
- Graph mining in Linked Data
- Interacting with Linked Data for Knowledge Discovery
Articles should be written following the Springer LNCS template (see authors instructions at
Submissions are exclusively admitted electronically, in PDF format, through the EasyChair system. The submission site is https://www.easychair.org/
Enrico Daga, The Open University, United Kingdom
Floriana Esposito, University of Bari, Italy
Nicola Fanizzi, University of Bari, Italy
Johannes Fürnkranz, Technische Universität Darmstadt, Germany
Agnieszka Lawrynowicz, Poznan University of Technology, Poland
Dunja Mladenic, Jozef Stefan Institute, Slovenia
Amedeo Napoli, University of Nancy, France
Matthias Nickles, National University of Ireland Galway, Ireland
Andriy Nikolov, Fluid Operations AG, Germany
Heiko Paulheim, University of Mannheim, Germany
Maria Teresa Pazienza, University of Rome Tor Vergata, Italy
Vojtěch Svátek, Prague University of Economics, Czech Republic
Isabelle Tellier, University of Paris III, France
Andrea Tettamanzi, University of Nice Sophia Antipolis, France
Volker Tresp, Ludwig Maximilian University of Munich, Germany
(more to be announced)
Ilaria Tiddi is a Ph.D. student at the Knowledge Media Institute of the Open University. Her research focuses on using Linked Data for Knowledge Discovery, more precisely, on using Linked Data knowledge to interpret patterns extracted through Data Mining. Her interests are in Linked Data, Semantic Technologies, Knowledge Discovery, Machine Learning and, additionally, Natural Language Processing. She was part of the organisation of the first LD4KD workshop and also currently assisting the organisation of the 11th Summer School on Ontology Engineering and the Semantic Web (SSSW2015).
Mathieu d’Aquin is a research fellow at theKnowledge Media Institute of the Open University. His current research focuses on building applications producing, consuming and reusing knowledge from Linked Data, particularly in the Educational domain (see LinkedUp! project or http://data.open.ac.uk/) and, more recently, Smart Cities Initiatives (MK:Smart). Besides publishing in major conferences and journals of the Semantic Web area, Mathieu has been recently organising events such as the Summer School on Ontology Engineering and the Semantic Web (SSSW2015), theLD4KD workshop, the Linked Learning workshop (2013, 2012, 2011), the Learning Analytics Data Challenge and the Using Linked Data in Learning Analytics tutorial.
Claudia d’Amato is a research assistant at the University of Bari – Computer Science Department. She pioneered the research on Machine Learning methods for Ontology Mining that still represents her main research interest. She is member of the editorial board of the Semantic Web Journal, chaired several Semantic Web conferences (ESWC, ISWC, ICSC) and was also a program committee member of conferences in the area of Artificial Intelligence such as AAAI, IJCAI, ECAI and ECML. She co-organised the International Workshop on Inductive Reasoning and Machine Learning on the Semantic Web (IRMLES) at ESCW’09-’11 and the International Uncertainty Reasoning Workshop (URSW) at ISCW’07-’11.
The workshop is kindly supported by www.KDnuggets.com : Analytics, Data Mining, & Data Science Resources