Deadline Extension: Linked Data Quality #LDQ2016 Call for Papers

To facilitate author requests and conditional accepted research papers 
notifications we extend our deadlines

* New submission deadline:
    - Abstracts: March 10, 2016 (Hawaii Time)
    - Full papers: March 16, 2016 (Hawaii Time)


3rd Workshop on Linked Data Quality
co-located with ESWC 2016, Heraklion, Crete, Greece

May 30, 2016

Important Dates
* Submission of research papers (abstract): March 10, 2016
* Submission of research papers (full paper): March 16, 2016
* Notification of paper acceptance: April 1, 2016
* Submission of camera-ready papers: April 15, 2016

In recent years, the Linked Data paradigm has emerged as a simple 
mechanism for employing the Web for data and knowledge integration, 
which allows the publication and exchange of information in an 
inter-operable way. This is confirmed by the growth of Linked Data on 
the Web, where currently more than 10,000 datasets are provided in RDF. 
This vast amount of valuable interlinked information gives rise to 
several use cases to discover meaningful relationships. However, in all 
these efforts, one crippling problem is the underlying data quality. 
Inaccurate, inconsistent or incomplete data strongly affect the results, 
leading to unreliable conclusions.

These quality problems affect every application domain, be it scientific 
(e.g., life science, environment), governmental, or industrial 
applications. Moreover, assessing the quality of these datasets and 
making the information explicit to the publisher and/or consumer is a 
major challenge. Quality is defined as “fitness for use†, thus 
DBpedia currently can be appropriate for a simple end-user application 
but could never be used in the medical domain for treatment decisions. 
However, quality is a key to the success of the data web and a major 
barrier for further industry adoption.

Despite the quality in Linked Data being an essential concept, few 
efforts are currently available to standardize how data quality tracking 
and assurance should be performed. Particularly in Linked Data, ensuring 
data quality is a challenge due to the openness of the Semantic Web, the 
diversity of the information and the unbounded, dynamic set of 
autonomous data sources and publishers and consumers (legal and software 
agents). Thus, there is a need for not only standardized concepts (e.g. 
vocabularies) but also methodologies, which can make the assessment 
explicit. None of the current approaches use the assessment to 
ultimately improve the quality of the underlying dataset, which when 
performed iteratively is essential for the management of the quality of 
these datasets.

The goal of the Workshop on Linked Data Quality is to raise the 
awareness of quality issues in Linked Data and to promote approaches to 
assess, monitor, manage, maintain and improve Linked Data quality.

The workshop topics include, but are not limited to:
* Concepts
- Quality modeling vocabularies
* Quality assessment
- Methodologies
- Frameworks for quality testing and evaluation
- Inconsistency detection
- Tools/Data validators
- Crowdsourcing data quality assessment
- Quality assessment leveraging background knowledge
- Assessing the quality evolution of Semantic Web Assets (Data, Services 
& Systems)
* Quality improvement
- Refinement techniques for Linked Datasets
- Methods and frameworks, e.g., linkage, alignment, cleaning, 
enrichment, correctness
- Service/system quality improvement methods and frameworks
- Error correction
- Tools
* Quality management
- Methodologies and frameworks to plan, control, assure or improve the 
quality of Semantic Web Assets
- Quality exploration and analysis interfaces
- Quality monitoring
- Developing, deploying and managing quality service ecosystems
- Use-case driven quality management
- Web Data and LOD quality benchmarks
- Managing sustainability issues in services
- Guarantee of service (availability, performance)
- Systems for transparent management of open data
* Other
- Quality of ontologies
- Reputation and trustworthiness of web resources
- User experience, empirical studies

Submission guidelines
We seek novel technical research papers in the context of Linked Data 
Quality with a length of up to 8 pages (long) and 4 pages (short) 
papers. Papers should be submitted in PDF format. Paper submissions 
should be formatted in the style of the Springer Publications format for 
Lecture Notes in Computer Science (LNCS). Please submit your paper via 
EasyChair at We note 
that the author list does not need to be anonymized, as we do not have a 
double-blind review process in place. Submissions will be peer reviewed 
by three independent reviewers. Accepted papers have to be presented at 
the workshop to be published in the proceedings. Proceedings will be 
published online at CEUR workshop proceedings series. The best papers 
accepted for this workshop will be included in the supplementary 
proceedings of ESWC 2016, which will appear in the Springer LNCS series.

Organizing Committee
* Anisa Rula – University of Milano-Bicocca, IT
* Amrapali Zaveri – Stanford University, USA
* Magnus Knuth – Hasso Plattner Institute, University of Potsdam, DE
* Dimitris Kontokostas – AKSW, University of Leipzig, DE

Program Committee
* Maribel Acosta – Karlsruhe Institute of Technology – AIFB, DE
* James Anderson – Datagraph, US
* Volha Bryl – Springer Science+Business Media, DE
* Ioannis Chrysakis – ICS FORTH, GR
* Mathieu d’Aquin – Knowledge Media Institute, The Open University, UK
* Jeremy Debattista – University of Bonn, Fraunhofer IAIS, DE
* Anastasia Dimou – MultimediaLab, Ghent University – iMinds, BE
* Suzanne Embury – University of Manchester, UK
* Christian Fürber – Information Quality Institute GmbH, DE
* Jose Emilio Labra Gayo – University of Oviedo, ES
* Markus Graube – Technische Universität Dresden, DE
* Tom Heath – The Open Data Institute, UK
* Tomáš Knap – Semantica, CZ
* Maristella Matera – Politecnico di Milano, IT
* John McCrae – CITEC, University of Bielefeld, DE
* Matteo Palmonari – University of Milan-Bicocca, IT
* Heiko Paulheim – University of Mannheim, DE
* Mariano Rico – Universidad Politécnica de Madrid, ES
* Patrick Westphal – AKSW, University of Leipzig, DE
* Antoine Zimmermann – École Nationale Supérieure des Mines de 
Saint-Étienne, FR

Dimitris Kontokostas
Department ofComputerScience, University of Leipzig & DBpedia Association
Research Group: AKSW/KILT

Received on Saturday, 5 March 2016 14:57:22 UTC