Theme and Topics

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 https://easychair.org/conferences/?conf=ldq2016. 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.