Since the start of the Linked Open Data (LOD) Cloud, we have seen an unprecedented volume of structured data published on the web, in most cases as RDF and Linked (Open) Data. The integration across this LOD Cloud, however, is hampered by the ‘publish first, refine later’ philosophy. This is due to various quality problems existing in the published data such as incompleteness, inconsistency, incomprehensibility, etc. These problems affect every application domain, be it scientific (e.g., life science, environment), governmental, or industrial applications.

We see linked datasets originating from crowdsourced content like Wikipedia and OpenStreetMap such as DBpedia and LinkedGeoData and also from highly curated sources e.g. from the library domain. 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 implemented. Particularly in Linked Data, ensuring data quality is a challenge as it involves a set of autonomously evolving data sources. Additionally, detecting the quality of datasets available and making the information explicit is yet another challenge. This includes the (semi-)automatic identification of problems. Moreover, none of the current approaches uses the assessment to ultimately improve the quality of the underlying dataset.

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, 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
Quality improvement
Refinement techniques for Linked Datasets
Linked Data cleansing
Error correction
Tools
Quality of ontologies
Reputation and trustworthiness of web resources
Best practices for Linked Data management
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 must be formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). Please submit your paper via EasyChair at https://easychair.org/conferences/?conf=ldq2015. Submissions that do not comply with the formatting of LNCS or that exceed the page limit will be rejected without review. 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.

Important Dates
All deadlines are, unless otherwise stated, at 23:59 Hawaii time.

Submission of research papers
Monday, March 16, 2015
Notification of paper acceptance
Thursday, April 9, 2015
Submission of camera-ready papers
Thursday April 30, 2015
Workshop date
Monday, June 1, 2015 (Morning Session)

More details can be found on the workshop website: http://ldq.semanticmultimedia.org/